Observer-based multiple-model adaptive output feedback control for a class of nonlinear systems

In this paper a systematic observer-based multiple-model adaptive controller design method is proposed for Lipschitz nonlinear systems. By introducing a compensator in the observer-based controller, the uncertainty due to the estimation error is decreased and the steady-state response is improved significantly. In order to deal with the uncertainty of system dynamics, a multiple-model switching scheme is introduced to improve the transient performance. A state-dependent dwell-time-based switching logic is used to ensure the asymptotic stability as it can cancel the possible increase of Lyapunov function in each switching. A simulation result is given to demonstrate the effectiveness of the proposed method.

[1]  Kumpati S. Narendra,et al.  Improving transient response of adaptive control systems using multiple models and switching , 1993, Proceedings of 32nd IEEE Conference on Decision and Control.

[2]  K. Narendra,et al.  Adaptive control of simple nonlinear systems using multiple models , 2002, Proceedings of the 2002 American Control Conference (IEEE Cat. No.CH37301).

[3]  R. Rajamani,et al.  A systematic approach to adaptive observer synthesis for nonlinear systems , 1995, Proceedings of Tenth International Symposium on Intelligent Control.

[4]  G. Besançon.  Remarks on nonlinear adaptive observer design , 2000 .

[5]  D. Mayne.  Nonlinear and Adaptive Control Design [Book Review] , 1996, IEEE Transactions on Automatic Control.

[6]  Karl Johan Åström,et al.  Adaptive Control , 1989, Embedded Digital Control with Microcontrollers.

[7]  A. Pascoal,et al.  RMMAC: a novel robust adaptive control scheme. Part II. Performance evaluation , 2004, 2004 43rd IEEE Conference on Decision and Control (CDC) (IEEE Cat. No.04CH37601).

[8]  P. Kokotovic,et al.  Nonlinear and Adaptive Control: An Abbreviated Status Report∗ , 2001 .

[9]  B. Anderson,et al.  Multiple model adaptive control. Part 1: Finite controller coverings , 2000 .

[10]  Thomas S. Brinsmead,et al.  Multiple model adaptive control with safe switching , 2001 .

[11]  Hu Guang-da.  Multiple Model Adaptive Control of CSTR , 2009 .

[12]  M. Branicky.  Multiple Lyapunov functions and other analysis tools for switched and hybrid systems , 1998, IEEE Trans. Autom. Control..

[13]  Michael Athans,et al.  The stochastic control of the F-8C aircraft using a multiple model adaptive control (MMAC) method--Part I: Equilibrium flight , 1977 .

[14]  Kumpati S. Narendra,et al.  New Concepts in Adaptive Control Using Multiple Models , 2012, IEEE Transactions on Automatic Control.

[15]  Kumpati S. Narendra,et al.  The changing face of adaptive control: The use of multiple models , 2011, Annu. Rev. Control..

[16]  A. Pascoal,et al.  RMMAC: a novel robust adaptive control scheme. Part I. Architecture , 2004, 2004 43rd IEEE Conference on Decision and Control (CDC) (IEEE Cat. No.04CH37601).

[17]  Thomas S. Brinsmead,et al.  Multiple model adaptive control. Part 2: switching , 2001 .

[18]  Kumpati S. Narendra,et al.  Adaptive control using multiple models , 1997, IEEE Trans. Autom. Control..

[19]  Brian D. O. Anderson,et al.  Challenges of adaptive control-past, permanent and future , 2008, Annu. Rev. Control..

[20]  Petros A. Ioannou,et al.  Multiple Model Adaptive Control With Mixing , 2010, IEEE Transactions on Automatic Control.

[21]  C. Brezinski.  Computational Aspects of Linear Control , 2002 .

[22]  Miroslav Krstic,et al.  Nonlinear and adaptive control de-sign , 1995 .

[23]  P. Olver.  Nonlinear Systems , 2013 .

[24]  Ye Xudong,et al.  Nonlinear adaptive control using multiple identification models , 2008, 2008 27th Chinese Control Conference.