An emergency braking controller is developed with improved operation characteristics near the maximum friction zone. The methodology is based on self-seeking a-priori unknown optimum operation point to maximize a performance function representing the optimal behavior of the considered dynamic system. Sliding mode with uncertain direction of control vector approach is utilized in the algorithm. An adaptive variable gain is utilized in the algorithm to improve its performance. Via the variable gain, both fast convergence to the a-priori unknown optimum operation point and reduced magnitude of oscillations in the braking moment inputs resulting less aggressive control action are achieved. Copyright © 2016 John Wiley & Sons, Ltd.

The adaptive stabilisation of uncertain second-order linear systems is addressed, under a lack of information from both states and parameters. The only standard assumptions are no zeros and the sign of the high frequency gain known. To the best of the authors' knowledge, there has been only an explicit solution proposed in the literature so far with proven stability. Despite the simplicity of the system, it does not fit in any of the standard nonlinear control methodologies available. Thus, this work is a complementary contribution providing a mixed control design strategy based on a reduced-order observer, adaptive Immersion & Invariance and Backstepping approaches. Hence, this solution depicts a transversal outlook of those nonlinear control strategies and provides a breakthrough for the generalisation of this non-trivial control problem. Numerical simulations are reported to assess the effectiveness of the adaptive strategy. Copyright © 2016 John Wiley & Sons, Ltd.

This work deals with the leader-follower and the leaderless consensus problems in networks of multiple robot manipulators. The robots are non-identical, kinematically different (heterogeneous), and their physical parameters are uncertain. The main contribution of this work is a novel controller that solves the two consensus problems, in the task space, with the following features: it estimates the kinematic and the dynamic physical parameters; it is robust to interconnecting variable-time delays; it employs the singularity-free unit-quaternions to represent the orientation; and, using energy-like functions, the controller synthesis follows a constructive procedure. Simulations using a network with four heterogeneous manipulators illustrate the performance of the proposed controller. Copyright © 2016 John Wiley & Sons, Ltd.

In this paper, we consider the transient performance of path-following control of nonholonomic mobile robots with parametric uncertainties. By incorporating prescribed performance bound (PPB) technique, the transient performances on position and orientational tracking errors will be guaranteed with convergence rates no less than certain pre-specified values and sufficiently small maximum overshoots. We also extend the proposed scheme to solve the formation control problem for a group of *N* unicycle-type mobile robots without inter-robot communications. It is ensured that all the robots can track their references with arbitrarily small errors and no collision will occur between any two robots. Simulation studies verify the established theoretical results. Copyright © 2016 John Wiley & Sons, Ltd.

In this paper, an adaptive dynamic surface control approach is developed for a class of multi-input multi-output nonlinear systems with unknown nonlinearities, bounded time-varying state delays, and in the presence of time-varying actuator failures. The type of the considered actuator failure is that some unknown inputs may be stuck at some time-varying values where the values, times, and patterns of the failures are unknown. The considered actuator failure can cover most failures that may occur in actuators of the systems. With the help of neural networks to approximate the unknown nonlinear functions and combining the dynamic surface control approach with the backstepping design method, a novel control approach is constructed. The proposed design method does not require a priori knowledge of the bounds of the unknown time delays and actuator failures. The boundedness of all the closed-loop signals is guaranteed, and the tracking errors are proved to converge to a small neighborhood of the origin. The proposed approach is employed for a double inverted pendulums benchmark as well as a chemical reactor system. The simulation results show the effectiveness of the proposed method. Copyright © 2016 John Wiley & Sons, Ltd.

In this work, we present a novel iterative learning control (ILC) scheme for a class of joint position constrained robot manipulator systems with both multiplicative and additive actuator faults. Unlike most ILC literature that requires identical reference trajectory from trail to trail, in this work the reference trajectory can be non-repetitive over the iteration domain without assuming the identical initial condition. A *t**a**n*-type Barrier Lyapunov Function is proposed to deal with the constraint requirements which can be both time and iteration varying, with ILC update laws adopted to learn the iteration-invariant system uncertainties, and robust methods used to compensate the iteration and time varying actuator faults and disturbances. We show that under the proposed ILC scheme, uniform convergence of the full state tracking error beyond a small time interval in each iteration can be guaranteed over the iteration domain, while the constraint requirements on the joint position vector will not be violated during operation. An illustrative example on a two degree-of-freedom robotic manipulator is presented to demonstrate the effectiveness of the proposed control scheme. Copyright © 2016 John Wiley & Sons, Ltd.

In this paper, the problem of dissipativity and passivity analysis is investigated for discrete-time complex-valued neural networks with time-varying delays. Both leakage and discrete time-varying delays have been considered. By constructing a suitable Lyapunov–Krasovskii functional and by using discretized Jensen's inequality approach, sufficient conditions have been established to guarantee the (*Q*,*S*,*R*) − *γ* dissipativity and passivity of the addressed discrete-time complex-valued neural networks. These conditions are derived in terms of complex-valued linear matrix inequalities (LMIs), which can be checked numerically using Yet Another LMI Parser toolbox in Matrix Laboratory. Finally, three numerical examples are established to illustrate the effectiveness of the obtained theoretical results. Copyright © 2016 John Wiley & Sons, Ltd.

In this work, a novel adaptive control scheme that allows driving a stand-alone variable-speed wind turbine system to its maximum power point is presented. The scheme is based on the regulation of the optimal rotor speed point of the wind turbine. In order to compute the rotor speed reference, a model-based extremum-seeking algorithm is derived. The wind speed signal is necessary to calculate this reference, and a novel artificial neural network is derived to approximate this signal. The neural network does not need off-line learning stage, because a nonlinear dynamics for the weight vector is proposed. A block-backstepping controller is derived to stabilize and to drive the system to the optimal power point; to avoid singularities, the gradient dynamics technique is applied to this controller. Numerical simulations are carried out to show the performance of the controller and the estimator. Copyright © 2016 John Wiley & Sons, Ltd.

This paper proposes an incipient sensor fault estimation and accommodation method for three-phase PWM inverter devices in electric railway traction systems. First, the dynamics of inverters and incipient voltage sensor faults are modeled. Then, for the augmented system formed by original inverter system and incipient sensor faults, an optimal adaptive unknown input observer is proposed to estimate the inverter voltages, currents and the incipient sensor faults. The designed observer guarantees that the estimation errors converge to the minimal invariant ellipsoid. Moreover, based on the output regulator via internal model principle, the fault accommodation controller is proposed to ensure that the *v*_{od} and *v*_{oq} voltages track the desired reference voltages with the tracking error converging to the minimal invariant ellipsoid. Finally, simulations based on the traction system in CRH2 (China Railway High-speed) are presented to verify the effectiveness of the proposed method. Copyright © 2016 John Wiley & Sons, Ltd.

In this paper, we propose a novel synchronization technique to investigate the projective synchronization of a class of uncertain network. First, the sliding mode control technique is modified. Then, the sliding surface between each node of the network and the synchronization target and the control input of the network are designed, and the uncertain parameter in the network can be effectively identified using the identification law of the uncertain parameter. In the end, a numerical example is given to illustrate the effectiveness of the synchronization technique proposed in our work. Copyright © 2016 John Wiley & Sons, Ltd.

In control design for vibration of beams in literature, the beam section is considered to have two axes of symmetry so that the bending and torsional vibrations are uncoupled; thus, the bending vibration is controlled independently without twisting the beam. However, if the cross section of a beam has only one axis of symmetry, the bending and torsional vibrations become coupled and the beam will undergo twisting in addition to bending. This paper addresses Lyapunov-based boundary control of coupled bending-torsional vibration of beams with only one axis of symmetry. The control strategy is based on applying a transverse force and a torque at the free end of the beam. The control design is directly based on the system partial differential equations (PDEs) so that spillover instabilities that are a result of model truncation are avoided. Three cases are investigated. Firstly, it is shown that when exogenous disturbances do not affect the beam, a linear boundary control law can exponentially stabilize the coupled bending-torsional vibration. Secondly, a nonlinear robust boundary control is established that exponentially stabilizes the beam in the presence of boundary and spatially distributed disturbances. Thirdly, to rule out the need for prior knowledge of disturbances upper-bound, the proposed robust control is redesigned to achieve an adaptive robust control that stabilizes the beam in the presence of disturbances with unknown upper-bound. The efficacy of the proposed controls is illustrated by simulation results. Copyright © 2016 John Wiley & Sons, Ltd.

In this paper, a novel adaptive disturbance attenuation algorithm is proposed combining switching and tuning. A two-level hierarchical switching logic is developed, which first selects in a short time the potentially best controller among a finite pre-designed family and then performs a local refinement of its attenuation capability. Thanks to the controller fine tuning, the proposed technique is able to provide a substantial performance improvement in terms of attenuation level as compared with a pure adaptive switching control scheme; at the same time, it retains the positive features of switching-based approaches, in particular, concerning the possibility of rapidly achieving a satisfactory behavior. Further, an arbitrary attenuation level is ensured in the presence of particular classes of disturbances and provided that it is compatible with robust stability requirement. Simulation results are shown to underline the potential of the approach as a solution to the problem. Copyright © 2016 John Wiley & Sons, Ltd.

The Special Issue presents results of current research on adaptive methods designed for periodic signals with unknown frequency. The first group of papers presents new methods for parameter estimation, while the second group focuses on practical applications involving active noise and vibration control problems. These papers are compiled in a special virtual issue of the journal at the journal homepage. To access all of the papers please follow the following link http://onlinelibrary.wiley.com/journal/10.1002/(ISSN)1099-1115/homepage/virtual_issue_-_recent_advances_in_adaptive_methods_for_frequency_estimation_wit.htm. Copyright © 2016 John Wiley & Sons, Ltd.

In this paper, we consider the design problem of making the convergence of the bounded-input, multi-input iterative learning controller presented in our previous work robust to errors in the model-based value of the input-output Jacobian matrix via a minimax (min-max or 'minimize the worst case') approach. We propose to minimize the worst case (largest) value of the infinity-norm of the matrix whose norm being less then unity implies convergence of the controller. This matrix is the one associated with monotonicity of a sequence of input error norms. The input-output Jacobian uncertainty is taken to be an additive linear one. Theorem 3.1 and its proof show that the worst-case infinity-norm is actually minimized by choosing either the inverse of the centroid of the set of possible input-output Jacobians or a zero matrix. And an explicit expression is given for both the criteria used to choose between the two matrices and the resulting minimum worst-case infinity norm. We showed previously that the matrix norm condition associated with monotonicity of a sequence of output-error norms is not sufficient to assure convergence of the bounded-input controller. The importance of knowing which norm condition is the relevant one is demonstrated by showing that the set of minimizers of the minimax problem formulated with the wrong norm does not contain in general minimizers of the maximum relevant norm and moreover can lead to a gain matrix that destroys the assured convergence of the bounded-input controller given in previous work. Copyright © 2016 John Wiley & Sons, Ltd.

Controller tuning and state estimation both benefit from knowledge about the dynamic parameters of the marine vessel. However, identifying these parameters can be a daunting task requiring precise open-loop measurements or collection of many output data samples induced by persistently exciting inputs. Performing these experiments in real-life conditions, every time payload of the vehicle changes, can be troublesome. Additional problems appear if the vessel is overactuated. This paper focuses on thruster modelling, actuator allocation and dynamic model identification for an overactuated marine vehicle. Firstly, we demonstrate a practical approach to mapping thrusters of an overactuated marine vehicle that in practice can generate different thrusts at identical inputs. Secondly, we address the issue of inverse actuator allocation for an overactuated surface marine platform and demonstrate a daisy-chain approach for achieving proper thrust distribution during simultaneous motion in all controllable degrees of freedom (DoF). Finally, we describe the application of a robust identification by use of self-oscillations (IS-O) method to identify actuated DoF. While previous work focused on using IS-O for identifying yaw DoF of a rudder-actuated autonomous catamaran and thruster-actuated micro-remotely operated underwater vehicle, in this paper, we extend the approach to identifying surge and sway DoF. This closed-loop identification procedure requires one experiment with four to five oscillations to completely identify inertial and nonlinear drag parameters of a marine vessel. Surge, sway and yaw DoFs of an overactuated autonomous surface marine vehicle *PlaDyPos* (developed at the Laboratory for Underwater Systems and Technologies) were identified using the IS-O method during sea trials in real-life conditions. Multiple experiments with varying initial settings were performed showing reproducibility of the identification procedure. Comparison of the results with the ordinary least squares identification procedure shows that root mean square error increase is negligible, especially if simplicity (explicit formulae for calculation of unknown parameters) and time parsimony of the IS-O method are taken into account. © 2016 The Authors. *International Journal of Adaptive Control and Signal Processing* Published by John Wiley & Sons, Ltd.

An adaptive backstepping controller is proposed to achieve the asymptotically stable trajectory tracking for a miniature helicopter. The helicopter model is firstly decomposed into a cascaded structure between the position and attitude loops with unmodeled dynamics. The backstepping technique is applied to exploit this cascaded structure and to streamline the controller design procedure. Hyperbolic tangent functions, instead of traditional sign functions, are adopted to complete adaptive algorithms for adjusting the upper bounds of the unmodeled dynamics. Further, the controller introduces an auxiliary dynamic system to ensure the thrust constraint and to obviate singularity during the command attitude extraction. The stability analysis demonstrates that, the asymptotical stability of the auxiliary dynamic system warrants the asymptotically stable tracking of the miniature helicopter system. Simulations verify the theoretical results and analyze the tracking performance. Copyright © 2016 John Wiley & Sons, Ltd.

Many practical batch processes operate repetitively in industry and lack intermediate measurements for the interested process variables. Moreover, the initial states as well as the desired product objective often vary with different runs because of the existence of many uncertainties in practice. This work proposes a novel adaptive terminal iterative learning control method to deal with random uncertainties in desired terminal points and initial states. The run-varying initial states are formulated by a stochastic high-order internal model, which is further incorporated into the controller design. The desired terminal output is run dependent and is directly compensated like a feedback term in the controller. Only the system output at the endpoint of an operation is utilized to update the control signal. An estimation algorithm is designed to update the system Markov parameters as a whole. No explicit model information is involved in the controller design; thus, the proposed method is data driven and can be applied to nonlinear systems directly. Both the theoretical analysis and the simulation studies demonstrate the effectiveness of the proposed approach under random initial states and iteration-varying referenced terminal points. Copyright © 2016 John Wiley & Sons, Ltd.

In order to deal with the overestimation of matched uncertainty and improve the convergence of sliding variable in sliding mode control, a modified structure of super-twisting algorithm (STA) with inner feedback and adaptive gain schedule is presented in this paper. The foremost characteristic of the modified STA is that an inner feedback mechanism is built in the standard STA so as to regulate the dynamic behavior of sliding variable effectively. The damping effect produced by the inner feedback can restrain the overshoot and enhance the performance of faster convergence of the sliding variable. Furthermore, the adaptive gain schedule can effectively decrease the chattering amplitude without knowing the upper bound of uncertainty. The numerical simulations and experiments on DC servo system with low speed are carried out to validate the effectiveness and performance advantages of the proposed methodology. Copyright © 2016 John Wiley & Sons, Ltd.

In this paper, we solve the problem of global output feedback regulation for uncertain feedforward nonlinear systems. The nonlinear functions, in the class of systems under consideration, are assumed to be dominated by an input-output function multiplied by an unknown parameter and a linear unmeasured states. Contrarily to the previous works, the interval of the output's power has been expanded from to . A numerical example is provided to illustrate the effectiveness of the proposed design scheme. Copyright © 2016 John Wiley & Sons, Ltd.

This paper deals with output regulation problem for an offshore managed pressure drilling system subject to sinusoidal disturbances. The disturbance is caused by the heave motion of the floating drilling rig during pipe connections, and the objective is to maintain a constant pressure at the bottom of the well. The controller for the heave disturbance attenuation consists of three cascaded parts. First, a nonlinear inversion element is applied to invert nonlinearity of choke. Second, an adaptive compensator is designed based on internal model, and certainty equivalence principles for asymptotic rejection of time-varying heave disturbance. Third, an output feedback controller is synthesized for providing stability and improving transient performance of closed-loop system. Robust stability of closed-loop system is analyzed using edge theorem. Simulation results of the combined adaptive output regulator are presented, which show satisfactory set-point tracking and attenuation of the heave disturbance. Copyright © 2016 John Wiley & Sons, Ltd.

Independent component analysis (ICA) is one of the most powerful methods for solving blind source separation problem. In various ICA methods, the Fast-ICA is an excellent algorithm, and it finds the demixing matrix that optimizes the nonlinear contrast function. There are three original contrast functions in the Fast-ICA to separate super-Gaussian and sub-Gaussian sources, and their respective derivatives are similar to nonlinearities used in neural networks. For the separation of large-scale super-Gaussian sources, however, the contrast functions and the nonlinearities are not optimal owing to high computational cost. To solve this potential problem, this paper proposes four rational polynomial functions to replace the original nonlinearities. Because the rational polynomials can be quickly evaluated, when they are used in the Fast-ICA, the computational load of the algorithms can be effectively reduced. The proposed rational functions are derived by the Pade approximant from Taylor series expansion of the original nonlinearities. To reduce the error of approximation, we make the behaviors of rational functions approach that of the original ones within an effective range as well as possible. The simulation results show that the Fast-ICA algorithms with rational nonlinearities not only can speed up the convergence but also improve the separation performance of super-Gaussian blind source separation. Copyright © 2016 John Wiley & Sons, Ltd.

Existing works on Wiener system identification have essentially been focused on the case where the output nonlinearity is memoryless. When memory nonlinearities have been considered, the focus has been restricted to backlash like nonlinearities. In this paper, we are considering Wiener systems where the output nonlinearity is a general hysteresis operator captured by the well-known Bouc–Wen model. The Wiener system identification problem is addressed by making use of a steady-state property, obtained in periodic regime, referred to as ‘hysteretic loop assumption’. The complexity of this problem comes from the system nonlinearity as well as its unknown parameters that enter in a non-affine way in the model. It is shown that the linear part of the system is accurately identified using a frequency method. Then, the nonlinear hysteretic subsystem is identified, on the basis of a parameterized representation, using a prediction-error approach. Copyright © 2016 John Wiley & Sons, Ltd.

This paper presents a new technique for online set membership parameter estimation of linear regression models affected by unknown-but-bounded noise. An orthotopic approximation of the set of feasible parameters is updated at each time step. The proposed technique relies on the solution of a suitable linear program, whenever a new measurement leads to a reduction of the approximating orthotope. The key idea for preventing the size of the linear programs from steadily increasing is to propagate only the binding constraints of these optimization problems. Numerical studies show that the new approach outperforms existing recursive set approximation techniques, while keeping the required computational burden within the same order of magnitude. Copyright © 2016 John Wiley & Sons, Ltd.

We solve the simultaneous closed-loop identification and tracking-control problems for fully-actuated Euler–Lagrange systems under input constraints. We use a nonlinear adaptive controller reminiscent of computed-torque-type controllers in which linear correction terms are saturated in order to comply with the imposed bounds on the control inputs. Adaptation, reminiscent of gradient methods, is used also with saturation. With respect to related literature, our contribution consists in establishing uniform global asymptotic stability. Therefore, our control scheme ensures robustness with respect to bounded perturbations and uniform convergence of the estimation errors for any initial conditions. Copyright © 2016 John Wiley & Sons, Ltd.

A decentralized prescribed performance adaptive tracking control problem is investigated for Markovian jump uncertain nonlinear interconnected large-scale systems. The considered interconnected large-scale systems contain unknown nonlinear uncertainties, unknown control gains, actuator saturation, and Markovian jump signals, and the Markovian jump subsystems are in the form of triangular structure. First, by defining a novel state transformation with the performance function, the prescribed performance control problem is transformed to stabilization problem. Then, introducing an intermediate control signal into the control design, employing neural network to approximate the unknown composite nonlinear function, and based on the framework of the backstepping control design and adaptive estimation method, a corresponding decentralized prescribed performance adaptive tracking controller is designed. It is proved that all the signals in the closed-loop system are bounded, and the prescribed tracking performances are guaranteed. A numerical example is provided to illustrate the effectiveness of the proposed control strategy. Copyright © 2016 John Wiley & Sons, Ltd.

In this paper, an adaptive observer is proposed for the joint estimation of states and parameters of a fractional nonlinear system with external perturbations. The convergence of the proposed observer is derived in terms of linear matrix inequalities (LMIs) by using an indirect Lyapunov method.The proposed adaptive observer is also robust against Lipschitz additive nonlinear uncertainty. The performance of the observer is illustrated through some examples including the chaotic Lorenz and Lü's systems. Copyright © 2016 John Wiley & Sons, Ltd.

The work described in this paper is motivated by the need to develop efficient and robust estimation filters with application to terrain-aided navigation of underwater robotic vehicles. One of the main problems addressed is the development of navigation particle filters that can deal with the scarcity of landmarks and the terrain ambiguity that characterize vast regions of the ocean floor. As a contribution to solve this problem, the paper proposes three novel particle filter algorithms and assesses their estimation efficiency and robustness to non-informative measurements using two well-known benchmarking tests. The performance of the new filters in these tests demonstrates their potential to solve a class of nonlinear problems that include, but are not limited to, the type of underwater navigation problem that motivated the present work. Our study concludes by examining the performance of the filters in terms of determining the position and velocity of an autonomous underwater vehicle in the presence of unknown ocean currents. When applied to terrain-aided navigation, the novel particle filter formulations formulations mitigate filter divergence issues frequently caused by terrain symmetries and are more robust than other well-known versions when used in scenarios with poor terrain information. The theoretical developments presented and the results obtained in simulations are validated using real data acquired during tests with an autonomous marine robot. Copyright © 2016 John Wiley & Sons, Ltd.

This paper presents a semi-adaptive control approach to closed-loop medication infusion problems. The rationale underlying this approach is to design a controller that can adapt model parameters with a large impact on the model's fidelity while fixing the remaining parameters at nominal values. In this paper, a control-oriented model for this purpose is derived via system identification and sensitivity analysis of a low-order model capturing the direct dose-response relationship Using the model thus derived, a model-reference adaptive controller and a composite adaptive controller are designed and compared with each other. In-silico simulation results using remifentanil's effect on respiratory rate as an example indicate that both controllers can regulate the output at commanded set points. Copyright © 2016 John Wiley & Sons, Ltd.

This paper investigates the robust adaptive fault-tolerant control problem for state-constrained continuous-time linear systems with parameter uncertainties, external disturbances, and actuator faults including stuck, outage, and loss of effectiveness. It is assumed that the knowledge of the system matrices, as well as the upper bounds of the disturbances and faults, is unknown. By incorporating a barrier-function like term into the Lyapunov function design, a novel model-free fault-tolerant control scheme is proposed in a parameter-dependent form, and the state constraint requirements are guaranteed. The time-varying parameters are adjusted online based on an adaptive method to prevent the states from violating the constraints and compensate automatically the uncertainties, disturbances, and actuator faults. The time-invariant parameters solved by using data-based policy iteration algorithm are introduced for helping to stabilize the system. Furthermore, it is shown that the states converge asymptotically to zero without transgression of the constraints and all signals in the resulting closed-loop system are uniformly bounded. Finally, two simulation examples are provided to show the effectiveness of the proposed approach. Copyright © 2016 John Wiley & Sons, Ltd.

The concept of linear parameter-varying (LPV) model has been developed as a convenient framework to describe a special class of uncertain LPV flight aircraft systems. In this paper, an adaptive control method for a class of uncertain LPV systems whose state-space matrix elements are unknown affine functions of a set of measurable scalar parameters is presented. Firstly, the scalar parameters are separated from the state matrices such that the LPV model is rewritten as general unknown parameter model, then state feedback adaptive control laws, in both cases: the matched uncertainty and the unmatched uncertainty, are designed with the aim of controlling the system state to follow a desired trajectory. The sufficient condition of stability is derived using a Lyapunov equation, not a parameterized Lyapunov equation. Simulation tests based on a simple example and a nonlinear model of a transport aircraft are given to illustrate the effectiveness of the control algorithm and to demonstrate that the adaptive controller satisfies the performance requirement for an aircraft control system. Copyright © 2016 John Wiley & Sons, Ltd.

This paper considers the robust adaptive control of Hammerstein nonlinear systems with uncertain parameters. The control scheme is derived from a modified criterion function which can overcome non-minimum phase property of the linear subsystem. The parameter adaptation is performed by using a robust recursive least squares algorithm with a deadzone weighted factor. The control law compensates the model error by incorporating the unmodeled dynamics estimation. An online pole assignment technique is also presented to guarantee that Assumption 2 always holds. Rigorous theoretical analysis indicates that the parameter estimation convergence and the closed-loop system stability can be guaranteed under mild conditions. Simulation examples including two typical continuous stirred tank reactor problems are studied to verify the effectiveness of the control scheme. Copyright © 2016 John Wiley & Sons, Ltd.

The paper presents a novel approach to justification of asymptotic stability of linear time-varying systems with persistently excited right-hand side. This approach combines the direct Lyapunov method with the Steklov averaging technique; its distinguishing feature is closed-form construction of the Lyapunov functional, along with resultant explicit estimates of the rate of convergency. © 2016 The Authors. International Journal of Adaptive Control and Signal Processing published by John Wiley & Sons, Ltd. © 2016 The Authors. International Journal of Adaptive Control and Signal Processing published by John Wiley & Sons, Ltd.

In this paper, an adaptive neural output-feedback control approach is considered for a class of uncertain multi-input and multi-output (MIMO) stochastic nonlinear systems with unknown control directions. Neural networks (NNs) are applied to approximate unknown nonlinearities, and K-filter observer is designed to estimate unavailable system's states. Due to utilization of Nussbaum gain function technique in the proposed approach, the singularity problem and requirement to prior knowledge about signs of high-frequency gains are removed, simultaneously. Razumikhin functional method is employed to deal with unknown state time-varying delays, so that the offered control approach is free of common assumptions on derivative of time-varying delays. Also, an adaptive neural dynamic surface control is developed; hence, explosion of complexity in conventional backstepping method is eliminated, effectively. The boundedness of all the resulting closed-loop signals is guaranteed in probability; meanwhile, convergence of the tracking errors to adjustable compact set in the sense of mean quartic value is also proved. Finally, simulation results are shown to verify and clarify efficiency of the offered approach. Copyright © 2016 John Wiley & Sons, Ltd.

In order to improve fault detection (FD) performance, integrated design of residual generation and evaluation is investigated in this paper for trade-offs between fault detection rate and false alarm rate (FAR). A set-membership approach is proposed in residual evaluation by adopting a threshold ellipsoid, which enables more design freedom than a conventional threshold value. With the set-based definitions of fault detection rate and FAR, the integrated design problem is formulated by maximizing the FD performance under a predefined FAR. The joint optimal selection of a residual generator and a threshold ellipsoid is equivalently transformed into a simplified optimization problem of determining an optimal threshold ellipsoid for any given residual generator. A suboptimal solution for the set-membership-based integrated FD system design is obtained based on approximated computation of the FD performance. Monte Carlo simulations show the performance improvement of the proposed method compared with an existing integrated design method. Copyright © 2016 John Wiley & Sons, Ltd.

In this paper, a novel algebraic method is proposed to estimate amplitudes, frequencies, and phases of a biased and noisy sum of complex exponential sinusoidal signals. The resulting parameter estimates are given by original closed formulas, constructed as integrals acting as time-varying filters of the noisy measured signal. The proposed algebraic method provides faster and more robust results, compared with usual procedures. Some computer simulations illustrate the efficiency of our method. Copyright © 2016 John Wiley & Sons, Ltd.

This paper investigates the problem of adaptive fault tolerant control for a class of dynamic systems with unknown un-modeled actuator faults. The fault model is assumed to be an unknown nonlinear function of control input, not in the traditional form in which the faults can be described as gain and/or bias faults. Using the property of the basic function of neural networks and the implicit function theorem, a novel neural networks-based fault tolerant controller is designed. Finally, the lateral dynamics of a front-wheeled steered vehicle is used to demonstrate the efficiency of the proposed design techniques. Copyright © 2016 John Wiley & Sons, Ltd.

In this paper, the evaluation and design scheme of fault diagnosability are investigated by differential geometry theories for a class of nonlinear affine uncertain systems with unknown indeterminate inputs. Considering two coupling relationships of outputs with uncertainties and faults, the sufficient and necessary conditions of fault detectability and isolability are established. For the problem that uncertainties influence the system outputs and make the faults undetectable, a design scheme of fault diagnosability is proposed. For nonlinear affine uncertain systems, a relatively complete theoretical system of the evaluation and design of fault diagnosability comes into being. An illustrative example is given to demonstrate the effectiveness of the proposed evaluation and design scheme of fault diagnosability. Copyright © 2016 John Wiley & Sons, Ltd.

The aim of this study was to design an adaptive control strategy based on recurrent neural networks (RNNs). This neural network was designed to obtain a non-parametric approximation (identification) of discrete-time uncertain nonlinear systems. A discrete-time Lyapunov candidate function was proposed to prove the convergence of the identification error. The adaptation laws to adjust the free parameters in the RNN were obtained in the same stability analysis. The control scheme used the states of the identifier, and it was developed fulfilling the necessary conditions to establish a behavior comparable with a quasi-sliding mode regime. This controller does not use the regular form of the switching function that commonly appears in the sliding mode control designs. The Lyapunov candidate function to design the controller and the identifier simultaneously requires the existence of positive definite solutions of two different matrix inequalities. As consequence, a class of separation principle was proven when the RNN-based identifier and the controller were designed by the same analysis. Simulations results were designed to show the behavior of the proposed controller solving the tracking problem for the trajectories of a direct current (DC) motor. The performance of the proposed controller was compared with the solution obtained when a classical proportional derivative controller and an adaptive first-order sliding mode controller assuming poor knowledge of the plant. In both cases, the proposed controller showed superior performance when the relation between the tracking error convergence and the energy used to reach it was evaluated. Copyright © 2016 John Wiley & Sons, Ltd.

This paper presents the proofs of robust stability of a discrete-time robust model reference controller combined with variable structure in an adaptive framework. All the proofs of robust stability are derived for the discrete-time case and are similar to those already existing for the conventional non-combined case. The controller is applied to a SISO LTI plant with unmodeled dynamics of multiplicative and additive types. It is shown that the combined controller can arbitrarily improve the convergence of the error while maintaining the robustness if compared with the non–combined case. Simulation results illustrate the performance of the proposed control strategy. Copyright © 2016 John Wiley & Sons, Ltd.

This paper considers iterative learning control for a class of uncertain multiple-input multiple-output discrete linear systems with polytopic uncertainties and actuator faults. The stability theory for linear repetitive processes is used to develop control law design algorithms that can be computed using linear matrix inequalities. A class of parameter-dependent Lyapunov functions is used with the aim of enlarging the allowed polytopic uncertainty range for successful design. The effectiveness and feasibility of the new design algorithms are illustrated by a gantry robot case study. Copyright © 2016 John Wiley & Sons, Ltd.

Rejection of unknown periodic disturbances in multi-channel systems has several industrial applications that include aerospace, consumer electronics, and many other industries. This paper presents a design and analysis of an output-feedback robust adaptive controller for multi-input multi-output continuous-time systems in the presence of modeling errors and broadband output noise. The trade-off between robust stability and performance improvement as well as practical design considerations for performance improvements are presented. It is demonstrated that proper shaping of the open-loop plant singular values as well as over-parameterizing the controller parametric model can significantly improve performance. Numerical simulations are performed to demonstrate the effectiveness of the proposed scheme. Copyright © 2016 John Wiley & Sons, Ltd.

This paper investigates the leader–follower consensus problem of uncertain nonlinear systems in strict-feedback form. By parameterizations of unknown nonlinear dynamics of the agents, an adaptive dynamic surface control with the aid of predictors, tracking differentiators is proposed to realize output consensus of the multi-agent systems. Unlike the existing adaptive consensus methods, the predictor errors are used to learn the unknown parameters, which can achieve fast learning without high-frequency signals in control inputs. As a fast precise signal filter, the tracking differentiator is used in the control design instead of first-order filters, which can further improve the control performance. Based on graph theory and Lyapunov stability theory, it is shown that the outputs of all followers ultimately synchronize to that of the leader with bounded tracking errors. Simulation results are provided to validate the effectiveness and advantage of the proposed consensus algorithm. Copyright © 2016 John Wiley & Sons, Ltd.

We consider the problem of output regulation for LTI systems in the presence of unknown *exosystems*. The knowledge about the *multi-frequency signals* exosystem consists in the maximum number of frequencies and their maximal value. The control scheme relies on two main components: an *estimation algorithm*, to reconstruct the signal generated by the exosystem, and a *controller*, to enforce the output regulation property to the closed-loop system. To tackle the first task, we propose a hybrid observer for the estimation of the (possibly piece-wise continuous) number and values of the frequencies contained in the exogenous signal. The hybrid observer is particularly appealing for numerical implementations, and it is combined with a self-tuning algorithm of the free parameters (gains), thus improving its performance even in case of noisy measurements. Semi-global exponential convergence of the estimation error is provided. As far as the second task is concerned, a robust hybrid regulator is designed for practical rejection of the multi-frequency disturbance signal acting on the plant. The result is achieved by exploiting the frequencies estimated by the hybrid observer. The effectiveness of the proposed control scheme is shown by means of numerical simulations. Copyright © 2016 John Wiley & Sons, Ltd.

This paper investigates the problem of robust reliable dissipative filtering for a class of Markovian jump nonlinear systems with uncertainties and time-varying transition probability matrix described by a polytope. Our main attention is focused on the design of a reliable dissipative filter performance for the filtering error system such that the resulting error system is stochastically stable and strictly
dissipative. By introducing a novel augmented Lyapunov–Krasovskii functional, a new set of sufficient conditions is obtained for the existence of reliable dissipative filter design in terms of linear matrix inequalities (LMIs). More precisely, a sufficient LMI condition is derived for reliable dissipative filtering that unifies the conditions for filtering with passivity and *H*_{∞} performances. Moreover, the filter gains are characterized in terms of solution to a set of linear matrix inequalities. Finally, two numerical examples are provided to demonstrate the effectiveness and potential of the proposed design technique. Copyright © 2016 John Wiley & Sons, Ltd.

This paper addresses a study of fault-tolerant control (FTC) for wireless networked control systems (WNCSs) in industrial automatic processes. The WNCSs is composed of many subsystems, which operate with different sampling cycles. In order to meet the real-time requirements and ensure a deterministic data transmission, the time division multiple access (TDMA) mechanism is adopted in WNCSs. The data in WNCSs are transmitted following a TDMA-based scheduler. According to the periodicity, WNCSs integrated with the scheduler is first formulated as discrete linear time periodic systems (LTPSs). Afterwards, a fault estimation method for LTPSs is developed under a *H*_{∞} performance specification with a regional pole constraint. With the achieved state observer and fault estimator, an FTC strategy for LTPSs is explored. Finally, the proposed methods are verified on a physical experimental WiNC platform. Copyright © 2016 John Wiley & Sons, Ltd.

In the past decade, managed pressure drilling, a technology aiming at precise well pressure control, has been gaining increasing popularity and been a key enabler for some of the most challenging well drilling cases such as the offshore deep water well drilling. This paper attempts to solve two of the main challenges involved in the managed pressure drilling systems: first, the bottom-hole states measurements are updated at a low rate, which can be practically viewed as unmeasured and thus need to be estimated in real time for both monitoring and control purposes and second, the drilling process is subject to uncertainties including unknown system parameters (e.g., frictions and densities), unmodeled actuator dynamics, and noise, which require a robust adaptive controller for control of the bottom-hole pressure. Towards this objective, an integrated estimator and adaptive control scheme is proposed. The estimator provides estimation of the bottom-hole pressure and flow rate, based on the available measurements from the topside. The adaptive controller drives the bottom-hole pressure to the desired value following a reference model, which also handles the time delays in the input signal. The design is based on a recently developed nonlinear drilling model. The results demonstrate that the adaptive controller has guaranteed performance bounds for both the input and the output signals of the system while using the estimation of the regulated outputs. Simulation results covering different operational conditions verify the theoretical findings. Copyright © 2016 John Wiley & Sons, Ltd.

The main purpose of this paper is to propose a direct and simple approach, called a self-tuning design approach, to dealing with any nonsymmetric dead-zone input nonlinearity where its information is completely unknown. In order to describe the approach, the output tracking problem is considered for a class of uncertain nonlinear systems with any nonsymmetric dead-zone input. First, a dead-zone input is represented as a time-varying input-dependent function such that the considered dynamical system with dead-zone input can be transfered into an uncertain nonlinear dynamical system subject to a linear input with time-varying input coefficient. Then, by making use of the self-tuning design approach, a class of adaptive robust output tracking control schemes with a rather simple structure is synthesized. Thus, the proposed direct and simple self-tuning design approach can be easily understood by the engineering designers, and the resulting simple adaptive robust control schemes can be well implemented in most practical engineering control problems. By combining the proposed self-tuning design approach with other control methods, one may expect to obtain a number of interesting results for a rather large class of uncertain nonlinear dynamical systems with dead-zone in the actuators. Finally,the simulations of some numerical examples are provided to demonstrate the validity of the theoretical results. Copyright © 2016 John Wiley & Sons, Ltd.

This work addresses state estimation in presence of outliers in observed data. Outlying data and measurements have a most relevant impact in many control and signal processing applications including marine systems related ones: underwater navigation systems exploiting acoustic data, for example, are frequently affected by outlying measurements. Other on-board sensors and devices are likely to produce measurements contaminated by outlier because of the harsh operating conditions of marine systems. Given the general interest for dealing with measurement outliers in a number of applications, this paper describes a state estimation solution exhibiting robustness to output outliers. The system model is assumed to be linear (either time varying or time invariant) discrete time. The proposed observer is designed by extending an outlier robust static parameter identification algorithm to the case of a linear dynamic plant. The designed estimator has a predictor/corrector structure like the Kalman filter and the Luenberger observer. Simulation and experimental results are provided illustrating the robustness of the derived solution to measurement outliers as compared with the Kalman filter. The proposed solution is also compared with alternative outlier robust state estimation filters showing its effectiveness, in particular, in the presence of measurements outliers occurring in a consecutive sequence. Because of its deterministic execution time and limited numerical complexity, the proposed state estimator can be readily applied in real-time applications. Copyright © 2016 John Wiley & Sons, Ltd.

This paper focuses on the control strategy needed by marine robots to be able to follow moving paths within a cooperative framework. This control aspect is essential in order to effectively perform emergency ship towing operations. In particular, these robots coordinate their motion, with the aim of performing an autonomous tying operation, linking the messenger line of a distressed ship to a salvage tugboat. Automatic guidance algorithms are developed in order to provide cooperation and coordination of robots' motion, in such a way to perform the knotting maneuver between the two employed vehicles. In particular, the major contribution of the present work in terms of adaptive control methodology consists in extending a well-known path following strategy for multi-vehicle cooperation to cope with moving reference paths. Extensive experimental testing validates the proposed concept, also pointing out the feasibility and effectiveness of the developed system in real-case scenarios. Copyright © 2016 John Wiley & Sons, Ltd.

In this paper, the problem of model reference adaptive control for nonlinear switched systems with parametric uncertainties is investigated. Asynchronous switching between subsystems and adaptive controllers is also considered. Firstly, a state feedback adaptive controller is designed. Then, sufficient conditions ensuring the global practical stability of the error switched system with average dwell time are proposed. The boundedness of all signals in the closed-loop system is guaranteed by the proposed adaptive controller. Finally, a practical example is given to demonstrate the validity of the main results. Copyright © 2016 John Wiley & Sons, Ltd.

This paper considers robust boundary control with disturbance adaptation to stabilize the vibration of a rectangular plate under disturbances with unknown upper-bounds. Disturbances are considered to be distributed both over the plate interior domain and along the boundary (in-domain and boundary distributed disturbances). Applying Hamilton's principle, the dynamics of the plate is represented in the form of a fourth-order partial differential equation subject to static and dynamic boundary conditions. The proposed model considers the membrane effect of axial force and the effect of actuator mass dynamics on the plate vibration. A robust boundary control is established that stabilizes the plate in presence of both in-domain and boundary disturbances. A rigorous Lyapunov stability analysis shows that the vibration of the plate is uniformly ultimately bounded and converges to the vicinity of zero by proper selection of control gains. For the vanishing in-domain disturbances, it is seen that exponential stability is achieved by the proposed control. Next, a disturbance adaptation law is introduced to stabilize the plate vibration in response to disturbances with unknown bounds, and the stability of the robust boundary control with disturbance adaptation is studied using Lyapunov. Simulation results verify the efficiency of the suggested control. Copyright © 2016 John Wiley & Sons, Ltd.

The likelihood calculation of a vast number of particles forms the computational bottleneck for the particle filter in applications where the observation model is complicated, especially when map or image processing is involved. In this paper, a numerical fitting approach is proposed to speed up the particle filter in which the likelihood of particles is analytically inferred/fitted, explicitly or implicitly, based on that of a small number of so-called fulcrums. It is demonstrated to be of fairly good estimation accuracy when an appropriate fitting function and properly distributed fulcrums are used. The construction of the fitting function and fulcrums are addressed respectively in detail. To avoid intractable multivariate fitting in multi-dimensional models, a nonparametric kernel density estimator such as the nearest neighbor smoother or the uniform kernel average smoother can be employed for implicit likelihood fitting. Simulations based on a benchmark one-dimensional model and multi-dimensional mobile robot localization are provided. Copyright © 2015 John Wiley & Sons, Ltd.

Marine craft feedback control systems typically require estimates of position, velocity and heading where the wave-induced motions should be suppressed. This paper presents a strapdown inertial navigation system with adaptive wave filtering. Wave filtering based on inertial navigation systems differ from previous vessel-model-based designs that require knowledge of vessel parameters and mathematical models for estimation of thruster and wind forces and moments based on auxiliary sensors. The origin of the inertial navigation system's error states is proven to be uniformly semiglobally exponentially stable. The wave-filtering scheme uses the estimated states of the inertial navigation system to separate the low-frequency motion of the craft from the wave-frequency motions. The observer structure also allows for estimation of the time-varying encounter frequency by using a signal-based frequency tracker or an adaptive observer. Finally, properties following from the triple-redundant sensor packages have been utilized to obtain optimal and robust sensor fusion with respect to sensor performance and faults. Copyright © 2015 John Wiley & Sons, Ltd.

We consider the problem of distributed state estimation over a sensor network in which a set of nodes collaboratively estimates the state of a continuous-time linear time-varying system. In particular, our work focuses on the benefits of weight adaptation of the interconnection gains in distributed Kalman filters. To this end, an adaptation strategy is proposed with the adaptive laws derived via a Lyapunov-redesign approach. The justification for the gain adaptation stems from a desire to adapt the pairwise difference of state estimates as a function of their agreement, thereby enforcing an interconnection-dependent gain. In the proposed scheme, an adaptive gain for each pairwise difference of the interconnection terms is used in order to address edge-dependent differences in the state estimates. Accounting for node-specific differences, a special case of the scheme is also presented, where it uses a single adaptive gain in each node estimate and which uniformly penalizes all pairwise differences of state estimates in the interconnection term. The filter gains can be designed either by standard Kalman filter or Luenberger observer to construct the adaptive distributed Kalman filter or adaptive distributed Luenberger observer. Stability of the schemes has been shown, and it is not restricted by the graph topology and therefore the schemes are applicable to both directed and undirected graphs. The proposed algorithms offer a significant reduction in communication costs associated with information flow by the nodes. Finally, numerical studies are presented to illustrate the performance and effectiveness of the proposed adaptive distributed Kalman filters. Copyright © 2015 John Wiley & Sons, Ltd.

The paper machine cross-directional (CD) process is a large-scale spatially distributed system. It is known to be severely ill-conditioned as the gain rolls down to zero for some of the process directions. Model uncertainties in the process are inevitable resulting in a challenging robust control design problem. CD actuators are subject to min–max constraints while slice lip actuators are subject to additional bending moment limits. Because of the large number of input constraints, the industrial practice is to tune the CD controller assuming inactive constraints. The robustness of CD feedback loops to model uncertainties under constrained internal model control satisfies an integral quadratic inequality. This work develops an automatic tuning algorithm that guarantees robust stability and performance of the constrained CD feedback loop. Spatial response models are identified in a prediction error frame delivering bounds on the CD process pseudo-singular values. The CD controller is synthesized online through a linear matrix inequalities feasibility problem taking into consideration the modal space uncertainty rising from the uncertainties in the estimated parameters and the expected variations in the dynamic response. The developed tuning technique is suitable for paper machines producing different grades of paper as the CD process spatial and dynamic responses change for each grade. The performance of the tuned constrained internal model control controller is validated through comparing it to an industrial CD controller that has been implemented in paper mills as part of a commercial product. Copyright © 2015 John Wiley & Sons, Ltd.

The paper is devoted to a new adaptive variable-structure system (VSS) control algorithm and its applications in underwater robotics. The proposed approach is based on the usage of sliding mode parameter (SMP) in order to have adaptation in VSS. It is demonstrated that the extreme values of SMPs are directly associated with high efficiency of transient processes in VSS. The essence of the presented adaptation algorithm consists on the adjustment of switching surface parameters such that the SMPs move from the assigned desirable value to the extreme values, with the overall effect to accelerate transients. The effectiveness of the derived approach is proven by applying it for controlling a real remotely operated vehicle. Copyright © 2015 John Wiley & Sons, Ltd.

This paper employs a unique decentralised cooperative control method to realise a formation-based collision avoidance strategy for a group of autonomous vehicles. In this approach, the vehicles' role in the formation and their alert and danger areas are first defined, and the formation-based intra-group and external collision avoidance methods are then proposed to translate the collision avoidance problem into the formation stability problem. The extension–decomposition–aggregation formation control method is next employed to stabilise the original and modified formations, whilst manoeuvring, and subsequently solve their collision avoidance problem indirectly. Simulation study verifies the feasibility and effectiveness of the intra-group and external collision avoidance strategy. It is demonstrated that both formation control and collision avoidance problems can be simultaneously solved if the stability of the expanded formation including external obstacles can be satisfied. Copyright © 2015 John Wiley & Sons, Ltd.

Effective and intelligent path planning algorithms designed for operation in a dynamic marine environment are essential for the safe operation of unmanned surface vehicles (USVs). Most of the current research deals with the ‘dynamic problem’ by basing solutions on the nonpractical assumption that each USV has a robust communication channel to obtain essential information such as position and velocity of marine vehicles. In this paper, a Kalman filter-based predictive path planning algorithm is proposed. The algorithm has been designed to predict the trajectories of moving ships, and the USV's own position in real time and accordingly assesses collision risk. For path planning, a weighted fast marching square method is proposed and developed to search for the optimal path. The path can be optimised for mission requirements such as minimum distance to travel and the most safety path by adjusting weighting parameters. The proposed algorithm has been validated using a number of simulations that include practical environmental aspects. The results show that the algorithms can sufficiently deal with complex traffic environments and that the generated practical path is suited for both unmanned and manned vessels. Copyright © 2015 John Wiley & Sons, Ltd.

We present a direct and an indirect nonlinear adaptive path-following controller for marine craft based on a line-of-sight guidance principle used by ancient navigators. The control laws are implemented using hydro-acoustic relative velocity measurements as opposed to absolute velocity measurements. For this purpose, a kinematic model for relative velocity in amplitude-phase form is derived. The first contribution is an adaptive indirect controller based on a disturbance observer designed for estimation and compensation of ocean currents. The equilibrium points of the cross-track and parameter estimation errors are proven to be globally *κ* exponentially stable. This guarantees that the estimated drift term converges to its true value exponentially. The observer is used in conjuncture with a control law to obtain asymptotic tracking and path following in the presence of ocean currents. The second contribution is a direct adaptive integral line-of-sight controller for path following. Global convergence of the cross-track error is proven by using Barbălat's lemma, which ensures that the parameter estimation error is bounded. Both methods can be applied to the horizontal-plane motion of surface vessels and autonomous underwater vehicles. An autonomous underwater vehicle case study is included to verify the results. Copyright © 2015 John Wiley & Sons, Ltd.

No abstract is available for this article.

]]>This paper presents a variation on adaptive backstepping output feedback control design for uncertain minimum-phase linear systems. Unlike the traditional nonlinear design, the proposed control method is linear and Lyapunov-based without utilizing overparametrization, tuning functions, or nonlinear damping terms to address parameter estimation error. Local stability of the closed-loop system and trajectory tracking are guaranteed. If the system dimension equals to the relative degree, the global stabilization and asymptotic convergence are achieved. Copyright © 2015 John Wiley & Sons, Ltd.

Hypersonic missile control in the terminal phase is addressed using continuous adaptive higher order sliding mode (AHOSM) control with adaptation. The AHOSM self-tuning controller is proposed and studied. The double-layer adaptive algorithm is based on equivalent control concepts and ensures non-overestimation of the control gain to help mitigating control chattering. The proposed continuous AHOSM control is validated via simulations of a hypersonic missile in the terminal phase. The robustness and high-accuracy output tracking in the presence of matched and unmatched external disturbances and missile model uncertainties is demonstrated. Copyright © 2016 John Wiley & Sons, Ltd.

Adaptive controllers have been developed to guarantee stability and asymptotically perfect tracking under ideal conditions. In particular, the simple adaptive control methodology has been developed to avoid the use of identifiers, observer-based controllers and, in general, to avoid using large order adaptive controllers in the control loop. In spite of initially successful applications, it is known that the basic adaptive algorithm may lead to divergence of the adaptive gains in such non-ideal conditions as the presence of disturbances. A sigma-term adjustment has been used to maintain boundedness with disturbance, yet is known that it seems to also eliminate perfect following even in ideal situations. Furthermore, bursting and other chaotic-like phenomena observed in connection with the sigma-term may also give pause to potential users of adaptive control. This paper revisits and modifies the use of various components of the simple adaptive control approach and shows how one can use passivity concepts such that, while it maintains robustness with disturbances, it also allows asymptotically perfect tracking in ideal conditions. Copyright © 2015 John Wiley & Sons, Ltd.

This paper introduces and focuses on a new control strategy for continuous-time Markov jump linear systems denominated minimax control. It generalizes switching and linear parameter varying control strategies and is determined such as to preserve stochastic stability and guaranteed performance. The special classes of Markov mode-dependent and mode-independent control are considered. The design methodology is characterized by minimax problems for which the existence of a saddle point is the central issue to be taken into account. As a natural application, Markov jump linear systems state feedback control design is discussed under this framework. A numerical example illustrates the theoretical results. Copyright © 2015 John Wiley & Sons, Ltd.

In this paper, the output-tracking problem for a class of non-affine nonlinear systems with unstable zero-dynamics is addressed. The system output must track a signal, which is the sum of a known number of sinusoids with unknown frequencies amplitudes and phases. The non-minimum phase nature of the considered systems prevents the direct tracking by standard sliding mode methods, which are known to generate unstable behaviors of the internal dynamics. The proposed method relies on the properties of differentially flat systems under mild assumptions relevant to the relation between the original output and a suitably designed flat output. As a result, the original problem is transformed into a state-tracking problem for invertible stable systems, where any internal state turns out to be bounded. Because of the uncertainty in the frequencies and in the parameters defining the relationship between system output and flat states, adaptive indirect methods are applied. Copyright © 2016 John Wiley & Sons, Ltd.

In this paper, we propose an adaptive output-feedback controller for uncertain linear systems without *a priori* knowledge of the plant high-frequency-gain sign. To deal with parametric uncertainties and unmodeled dynamics, we consider a robust adaptive strategy named *binary model reference adaptive control*, which has the good transient properties and robustness of sliding mode control with the important advantage of having a continuous control signal free of chattering. The effective way of tackling unknown high-frequency-gain sign is employing monitoring functions. The developed adaptive control guarantees global exponential stability of the closed-loop error system with respect to a compact residual set. Moreover, in the absence of unmodeled dynamics, exact tracking of a reference signal can be achieved. Numerical simulations illustrate the efficacy of the proposed approach. Copyright © 2015 John Wiley & Sons, Ltd.

Adaptive control schemes are developed for uncertain multivariable systems with unmatched input disturbances and are applied to an aircraft flight turbulence compensation problem. Key relative degree conditions from system input and disturbance are derived in terms of system interactor matrices for the design of a nominal state or output feedback control law that ensures desired asymptotic output tracking and disturbance rejection. To deal with an uncertain system high-frequency gain matrix, a gain matrix decomposition technique is employed to parametrize an error system model in terms of the parameter and tracking errors, for the design of an adaptive parameter update law with reduced system knowledge. Both adaptive state and output feedback control schemes are presented in detail for systems with general interactor matrices, based on an LDS gain decomposition parametrization, and LDU and SDU decomposition-based designs are also discussed, to develop unified adaptive disturbance rejection techniques for multivariable systems. All closed-loop system signals are bounded, and the system output tracks a reference output asymptotically despite the system and disturbance parameter uncertainties. Simulation results of an aircraft flight control system with adaptive turbulence compensation are presented to show the desired system disturbance rejection performance. Copyright © 2015 John Wiley & Sons, Ltd.

Unlike previous work in the area of high-gain observers, the focus of this paper is the effect measurement noise has on the tracking error, not the estimation error. Although a tradeoff exists between the speed of state reconstruction and the bound on the steady-state estimation error, such a compromise is not evident in the tracking error of the first state. The purpose of this work is to provide the reader with the relationship between the high-gain observer parameter and the tracking error and its subsequent derivatives. Copyright © 2015 John Wiley & Sons, Ltd.

This paper is concerned with the design of variable structure controllers for uncertain switched linear plants. The proposed method is based on Lyapunov–Metzler inequalities and on properties of strictly positive real (SPR) systems, with the advantage that it can be applied in control of uncertain switched linear system. The first contribution shows that there exists a solution of the SPR synthesis for a class of linear uncertain switched systems using switched static output feedback, if and only if there exists a solution of this SPR synthesis with switched static state feedback. This result is a generalization of known conditions regarding this problem, for linear time-invariant and known (without uncertainties) plants, and offers a possible procedure for output feedback SPR synthesis: first solve the state feedback SPR synthesis and then, using the obtained solution, find a solution for the output feedback SPR synthesis. Next are presented necessary and sufficient (sufficient) conditions for the SPR synthesis for a class of linear uncertain switched systems using switched static output feedback, for plants with the same number of inputs and outputs (with the number of outputs greater than the number of inputs). Two examples illustrate the effectiveness of the robust control system, including applications of the proposed methods, considering switched static state feedback and switched static output feedback, in the design of switching control strategies for active suspensions systems in road vehicles. Performance indices such as decay rate, guaranteed cost, and restriction in the norm of controller gains are considered in these designs. Copyright © 2016 John Wiley & Sons, Ltd.

In this work, we consider the 3D visual tracking problem for a robot manipulator with uncertainties in the kinematic and dynamic models. The visual feedback is provided by a fixed and uncalibrated camera located above the robot workspace. The Cartesian motion of the robot end effector can be separated into a 1D motion parallel to the optical axis of the camera and a 2D motion constrained on a plane orthogonal to this axis. Thus, the control design can be simplified, and the overall visual servoing system can be partitioned in two almost-independent subsystems. Adaptive visual servoing schemes, based on a kinematic approach, are developed for image-based look-and-move systems allowing for both depth and planar tracking of a reference trajectory, without using image velocity and depth measurements. In order to include the uncertain robot kinematics and dynamics in the presented solution, we develop a cascade control strategy based on an indirect/direct adaptive method. The stability and convergence properties are analyzed in terms of Lyapunov-like functions and the passivity-based formalism. Numerical simulations including hardware-in-the-loop results, obtained with a robot manipulator and a web camera, are presented to illustrate the performance and feasibility of the proposed control scheme. Copyright © 2016 John Wiley & Sons, Ltd.

This paper addresses the design of a model-based event-triggered sliding mode control strategy of adaptive type. The overall proposal can be regarded as a networked control scheme, because one of the design objectives is to reduce the number of transmissions of the plant state over the network used to construct the control loop. The key idea consists in using the actual plant state or the state of a suitably updated nominal model of the plant to generate the control variable, depending on the magnitude of the sliding variable. The proposed adaptive control scheme proves to be capable of steering the sliding variable to a boundary layer of the sliding manifold in finite time, in spite of the presence of bounded uncertainties with unknown bounds. When the sliding variable is confined within the boundary layer, the nominal model state is used to compute the control variable. Then, a continuous control can be analytically determined and applied, which provides a beneficial chattering alleviation effect. The proposed networked control scheme is theoretically analyzed and assessed in simulation with satisfactory results. Copyright © 2016 John Wiley & Sons, Ltd.

A variable structure model-reference adaptive control (VS-MRAC) of impedances and admittances (driving-point (DP) functions) is proposed. Only voltage and current measurements are required to implement the controllers. The inclusion of a prefilter in the reference model allows the synthesis of quite general DP functions, even with nonminimum phase zeros and unstable poles. It is shown that the stability of the closed-loop system depends only on the source DP function and the chosen reference model. The VS-MRAC guarantees global exponential stability properties, robustness to parameter uncertainties, disturbances, and unmodelled dynamics. Simulation results illustrate the performance and robustness of the VS-MRAC. Copyright © 2016 John Wiley & Sons, Ltd.

In this paper, an adaptive output feedback control scheme is proposed for a class of nonlinear systems with possible actuator failures. The system not only involves unknown parameters but also takes nonlinear terms linear in the unmeasured states into account and is preceded by hysteretic actuators whose nonlinearities are characterized by the saturated Prandtl–Ishlinskii model. By developing a high-gain observer with one dynamic gain, the closed-loop stability and arbitrarily small tracking error can be guaranteed. Furthermore, by estimating the upper bound of the unknown time-varying (piecewise constant) parameters caused by the actuator failures, together with an initialization technique, it is proved that the tracking performance is achievable, which is fundamentally different from the commonly accepted concept that with actuator failures, it is impossible to improve the tracking performance by using initialization technique. Copyright © 2015 John Wiley & Sons, Ltd.

This paper presents new methodologies to design a set of controllers such that every controller in the set preserves closed-loop stability of a given multivariable plant under prescribed loop failures. The proposed approaches differ from existing techniques in two ways: First, our methods are strictly based on frequency response data of the plant that can be easily measured by experiments. No mathematical models or system identification processes are used. Second, while most control design methods find one controller, we design a set of controllers satisfying the control objective. Two approaches are presented with examples illustrating the respective advantages. Copyright © 2015 John Wiley & Sons, Ltd.

Accurate estimation of circadian phase is critical to the assessment and treatment of circadian disruption. Direct measurements of circadian rhythm markers such as dim light melatonin onset and core body temperature are inconvenient and acquired at best at low rate. On the other hand, measurements of other circadian rhythm-modulated signals such as actigraphy, heart rate, and body temperature are convenient but are typically masked by many other factors.

In this paper, we present a new multi-input adaptive notch filter algorithm that can be used to extract the periodic components from multiple circadian signals simultaneously. We also prove some stability properties of the proposed filter. Once the periodic components are extracted, the next step is to relate their phases with the circadian phase. For this, we propose a nonlinear observer, which is based on a model of the circadian phase dynamics. The model takes the form of a first-order ODE, incorporating the concept of phase response curve, which is widely used in the study of biological oscillators. We also prove the stability of the observer. We evaluate our algorithms using simulation data generated from a circadian rhythm model for fruit flies (*Drosophila melanogaster*). Copyright © 2016 John Wiley & Sons, Ltd.

This work proposes a control structure to be applied to robotic manipulators, which are articulated mechanical systems composed of links connected by joints. The proposed controller can be divided into two parts. The first one is a left inverse system, which is used to decouple the dynamic behavior of the joints. The second is a sliding mode controller, which is applied for each decoupled joint. It is important to note that the proposed structure, using only input/output measurements, reduces the control signal ‘chattering’, and it is robust to parametric uncertainties. Besides all the characteristics presented, the proposed structure simplifies the design of sliding mode controller to be applied in robotic manipulators. All these features are verified by simulations. Copyright © 2016 John Wiley & Sons, Ltd.

This paper considers a class of uncertain nonlinear systems exactly described by Takagi–Sugeno (T-S) fuzzy models, with or without matched uncertainties and/or disturbances, within an operating region in the state space. Considering the plant subject to actuator saturation is proposed, a switched control design method such that the equilibrium point of the controlled systems is locally asymptotically stable with an adequate decay rate, for all initial conditions in a region obtained in the design procedure, that is within a given operating region. Moreover, an exact representation of the minimum function using signal functions is presented. Therefore, it is offered a bridge between the switched control and variable structure control laws, because they are usually based on minimum and signal functions, respectively. Considering the well-known approximation of the signal function by a sigmoid function, this paper introduces the ‘smooth minimum’ function. The smooth minimum function allowed the design of ‘smooth switched’ control laws, without chattering, for the aforementioned class of systems. The proposed smooth switched control laws guarantee the uniform ultimate boundedness of the controlled systems. Design examples and simulation results illustrate the control design procedures and the effectiveness of the proposed control laws. Future researches in this subject may explore the presented relationship between the switched control and variable structure control laws, in order to obtain new useful control laws. Copyright © 2016 John Wiley & Sons, Ltd.

In this paper, a new class of estimators for permanent magnet synchronous motors is proposed. Using a novel representation of the permanent magnet synchronous motor dynamics and some suitable filtering, we obtain new solutions to two important problems: (a) estimation of the stator resistance and inductance and (b) flux estimation with known electrical parameters. Neither one of the schemes requires knowledge of the mechanical parameters nor of the magnetic flux constant. Conditions for *global convergence* are given in terms of persistency of excitation requirements that, for the flux observation problem, is imposed only to the rotor speed. Both stability proofs require the availability of rotor speed. However, for the flux observer, extensive simulations using the speed estimate obtained from the estimator (instead of the actual rotor speed) illustrate the robustness of the proposed scheme *vis-à-vis* this assumption. Simulations also show that the new flux observer outperforms other existing designs. Copyright ©2015 John Wiley & Sons, Ltd.

This paper views the classical Chiu–Jain algorithm, originally proposed for congestion control of network links, as a decentralized algorithm for the fair allocation of a total of *c* units of a shared resource among *n* users. A new analysis is given of the general case of additive increase and multiplicative decrease (AIMD) dynamics, from the perspective of virtual equilibria and variable structure systems, leading to a better understanding of the Chiu–Jain algorithm, which is one example of AIMD dynamics. It is shown that the variable structure discrete dynamical system that describes the evolution of the share of each individual, starting from an arbitrary initial allocation, always attains a neighbourhood of the fair share (*c*/*n*) for each user, under the assumption that the latter is known. Subsequently, a new adaptive version of the algorithm, called adaptive AIMD, is described, with the same property of converging to the fair share, without assuming that it is known. Simulations that show the behaviour and advantages of the proposed adaptive AIMD adaptive algorithm are given. Copyright © 2015 John Wiley & Sons, Ltd.

This paper considers the problem of reconstructing state information in all the nodes of a complex network of dynamical systems. The individual nodes comprise a known linear part and unknown but bounded uncertainties in certain channels of the system. A supervisory adaptive sliding mode observer configuration is proposed for estimating the states. A linear matrix inequality (LMI) approach is suggested to synthesise the gains of the sliding mode observer. Although deployed centrally to estimate all the states of the complex network, the design process depends only on the dynamics of an individual node of the network. The methodology is demonstrated by considering a network of Chua oscillators. Copyright © 2016 John Wiley & Sons, Ltd.

The main contribution of the paper is to propose a scheme of attitude controller for a class of unmanned aerial vehicles based on an adaptive version of the super-twisting algorithm. This controller is based on a very recent second-order sliding mode controller, which is robust in spite of uncertainties and perturbations, ensures finite time convergence, reduces the chattering, increases the accuracy, and does not require time derivative of the sliding variable. A very important feature of the controller is its adaptive gain, which allows to design the controller without knowing bounds of the uncertainties and perturbations. This controller is validated by experimental results. Comparisons versus PID-based controller are made in order to evaluate the robustness of the closed-loop system when similar perturbations are acting. Copyright © 2015 John Wiley & Sons, Ltd.

This paper proposes a new approximate dynamic programming algorithm to solve the infinite-horizon optimal control problem for weakly coupled nonlinear systems. The algorithm is implemented as a three-critic/four-actor approximators structure, where the critic approximators are used to learn the optimal costs, while the actor approximators are used to learn the optimal control policies. Simultaneous continuous-time adaptation of both critic and actor approximators is implemented, a method commonly known as synchronous policy iteration. The adaptive control nature of the algorithm requires a persistence of excitation condition to be a priori guaranteed, but this can be relaxed by using previously stored data concurrently with current data in the update of the critic approximators. Appropriate robustifying terms are added to the controllers to eliminate the effects of the residual errors, leading to asymptotic stability of the equilibrium point of the closed-loop system. Simulation results show the effectiveness of the proposed approach for a sixth-order dynamical example. Copyright © 2015 John Wiley & Sons, Ltd.

An adaptive second-order sliding mode output feedback controller is developed to deal with the case that the bound of the uncertainty/perturbation is unknown. The control structure consists in a twisting controller and a super-twisting observer to estimate the unmeasured variable. The gains of the controller and observer are parametrized in terms of a scalar gain, such that increasing these two gains, it is always possible to find values to (finite-time) stabilize the closed loop system. Finally, adaptive gain laws are provided to increase the controller and the observer gains until the closed loop has been stabilized. The main technical contribution of the paper is to give a sound and non-trivial Lyapunov analysis of this otherwise intuitively simple idea. We illustrate the performance of the proposed controller by means of experimental results in a laboratory setup. Copyright © 2016 John Wiley & Sons, Ltd.