Proportional-Integral observer design for nonlinear uncertain systems modelled by a multiple model approach

In this paper, a decoupled multiple model approach is used in order to cope with the state estimation of uncertain nonlinear systems. The proposed decoupled multiple model provides flexibility in the modelling stage because the dimension of the submodels can be different and this constitutes the main difference with respect to the classically used multiple model scheme. The state estimation is performed using a proportional integral observer (PIO) which is well known for its robustness properties with respect to uncertainties and perturbations. The Lyapunov second method is employed in order to provide sufficient existence conditions of the observer, in LMI terms, and to compute the optimal gains of the PIO. The effectiveness of the proposed methodology is illustrated by a simulation example.

[1]  Kazuo Tanaka,et al.  Robust stabilization of a class of uncertain nonlinear systems via fuzzy control: quadratic stabilizability, H∞ control theory, and linear matrix inequalities , 1996, IEEE Trans. Fuzzy Syst..

[2]  Marcin Witczak,et al.  A Hybrid Neuro-Fuzzy and De-Coupling Approach Applied to the DAMADICS Benchmark Problem , 2003 .

[3]  Didier Maquin,et al.  State estimation of nonlinear discrete-time systems based on the decoupled multiple model approach , 2007, ICINCO-SPSMC.

[4]  Lihua Xie,et al.  Robust H/sub infinity / control for linear systems with norm-bounded time-varying uncertainty , 1992 .

[5]  Changchun Hua,et al.  Synchronization of chaotic systems based on PI observer design , 2005 .

[6]  P. Gawthrop Continuous-time local state local model networks , 1995, 1995 IEEE International Conference on Systems, Man and Cybernetics. Intelligent Systems for the 21st Century.

[7]  C.E. de Souza,et al.  Robust H/sub infinity / control for linear systems with norm-bounded time-varying uncertainty , 1990, 29th IEEE Conference on Decision and Control.

[8]  Alexander Weinmann Uncertain Models and Robust Control , 2002 .

[9]  S. P. Linder,et al.  Rejecting disturbances to flexible structures using PI Kalman filters , 1997, Proceedings of the 1997 IEEE International Conference on Control Applications.

[10]  Roderick Murray-Smith,et al.  Multiple Model Approaches to Modelling and Control , 1997 .

[11]  Stephen P. Boyd,et al.  Linear Matrix Inequalities in Systems and Control Theory , 1994 .

[12]  Lihua Xie,et al.  Robust Kalman filtering for uncertain discrete-time systems , 1994, IEEE Trans. Autom. Control..

[13]  Kunsoo Huh,et al.  Dissipative proportional integral observer for a class of uncertain nonlinear systems , 2007, 2007 American Control Conference.

[14]  E. Yaz Linear Matrix Inequalities In System And Control Theory , 1998, Proceedings of the IEEE.

[15]  Vladimir A. Yakubovich,et al.  Linear Matrix Inequalities in System and Control Theory (S. Boyd, L. E. Ghaoui, E. Feron, and V. Balakrishnan) , 1995, SIAM Rev..

[16]  B. Shafai,et al.  Robust control system design with a proportional integral observer , 1989 .

[17]  F. Doyle,et al.  Multiple Model Approach for CSTR Control , 1999 .

[18]  Didier Maquin,et al.  State estimation for non-linear systems using a decoupled multiple model , 2008, Int. J. Model. Identif. Control..

[19]  J. Ragot,et al.  Estimating the state and the unknown inputs of nonlinear systems using a multiple model approach , 2008, 2008 16th Mediterranean Conference on Control and Automation.

[20]  Taehyun Shim,et al.  Dissipative Proportional Integral Observer for a Class of Uncertain Nonlinear Systems , 2007, ACC.

[21]  K. Burnham,et al.  EXTENDED GLOBAL TOTAL LEAST SQUARE APPROACH TO MULTIPLE-MODEL IDENTIFICATION , 2005 .

[22]  Dimitar Filev Fuzzy modeling of complex systems , 1991, Int. J. Approx. Reason..

[23]  Ravindra D. Gudi,et al.  Identification of complex nonlinear processes based on fuzzy decomposition of the steady state space , 2003 .

[24]  Michio Sugeno,et al.  Fuzzy identification of systems and its applications to modeling and control , 1985, IEEE Transactions on Systems, Man, and Cybernetics.

[25]  Kurosh Madani,et al.  Implementation of recurrent multi-models for system identification , 2007, ICINCO-SPSMC.

[26]  Bahram Shafai,et al.  Robust control system design with proportional integral observer , 1988, Proceedings of the 27th IEEE Conference on Decision and Control.

[27]  T. Johansen,et al.  Constructing NARMAX models using ARMAX models , 1993 .

[28]  Didier Maquin,et al.  NON-LINEAR SYSTEM IDENTIFICATION USING UNCOUPLED STATE MULTIPLE-MODEL APPROACH , 2006 .