Neural-network-based control schemes are generally designed by replacing standard elements of the classic control schemes by feedforward neural networks. The introduction of discrete time recurrent networks, which are inherently dynamic systems, into those schemes can simplify the design of neural controllers. The concept of applying recurrent networks in indirect adaptive control schemes is described. A combined network cluster consisting of the control network and the model network is constructed to allow the use of the real-time recurrent learning algorithm. To demonstrate the feasibility of the method two simulation examples are presented.
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