Research Article

Robust stability of nonlinear model predictive control with extended Kalman filter and target setting

  1. Rui Huang1,*,
  2. Sachin C. Patwardhan2,
  3. Lorenz T. Biegler3

Article first published online: 2 APR 2012

DOI: 10.1002/rnc.2817

Issue

International Journal of Robust and Nonlinear Control

International Journal of Robust and Nonlinear Control

Volume 23, Issue 11, pages 1240–1264, 25 July 2013

Additional Information

How to Cite

Huang, R., Patwardhan, S. C. and Biegler, L. T. (2013), Robust stability of nonlinear model predictive control with extended Kalman filter and target setting. Int. J. Robust Nonlinear Control, 23: 1240–1264. doi: 10.1002/rnc.2817

Author Information

  1. 1

    United Technologies Research Center, 411 Silver Lane, East Hartford, CT 06108, USA

  2. 2

    Department of Chemical Engineering, Indian Institute of Technology, Bombay, Mumbai, Maharashra, 400076, India

  3. 3

    Department of Chemical Engineering, Carnegie Mellon University, 5000 Forbes Avenue, Pittsburgh, PA, 15213, USA

* Correspondence to: R. Huang, United Technologies Research Center, 411 Silver Lane, East Hartford, CT 06108, USA.
E-mail: huangr@utrc.utc.com, huangr76@gmail.com

Publication History

  1. Issue published online: 14 JUN 2013
  2. Article first published online: 2 APR 2012
  3. Manuscript Accepted: 18 FEB 2012
  4. Manuscript Revised: 14 FEB 2012
  5. Manuscript Received: 3 OCT 2009

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Keywords:

  • NMPC, EKF, robust stability, target setting

SUMMARY

This work deals with the closed-loop robust stability of nonlinear model predictive control (NMPC) coupled with an extended Kalman filter (EKF). First, we point out the gaps between the practical formulations and theoretical research. Then, we show that the estimation error dynamics of an EKF are input-to-state stable (ISS) in the presence of nonvanishing perturbations. Moreover, a target setting optimization problem is proposed to solve the target state corresponding to the desired set points, which are used in the objective function in NMPC formulation. Thus, the objective function is a Lyapunov function candidate, and the input-to-state practical stability (ISpS) of the closed-loop system can be established. Moreover, we see that the stability property deteriorates because of the estimation error. Simulation results of the proposed scheme are presented.Copyright © 2012 John Wiley & Sons, Ltd.

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