A moving horizon ℋ∞ observer for discrete-time systems

In this paper, we address the problem of robust moving horizon observer for nonlinear discrete-time systems. The main contribution lies in the use of a robust moving horizon approach in a Luenberger structure observer. Thanks to this new design, a new nonrestrictive synthesis condition, expressed in term of Bilinear Matrix Inequality (BMI), is obtained. Indeed, the obtained BMI contains more degree of freedom than those established by the approaches available in the literature which consider a traditional ℋ∞ observer, with only one measurement. In this paper, we consider first the linear noisy case, then an extension to nonlinear systems is given. Finally, numerical examples show the better performances of our approach.

[1]  Mohamed Boutayeb,et al.  Synchronization and input recovery in digital nonlinear systems , 2004, IEEE Transactions on Circuits and Systems II: Express Briefs.

[2]  Ali Zemouche,et al.  Observer Design for Lipschitz Nonlinear Systems: The Discrete-Time Case , 2006, IEEE Transactions on Circuits and Systems II: Express Briefs.

[3]  Alfredo Germani,et al.  A robust observer for discrete time nonlinear systems , 1995 .

[4]  M. Boutayeb,et al.  Static output feedback stabilization for linear discrete-time systems , 2004, 2004 43rd IEEE Conference on Decision and Control (CDC) (IEEE Cat. No.04CH37601).

[5]  J. Grizzle,et al.  The Extended Kalman Filter as a Local Asymptotic Observer for Nonlinear Discrete-Time Systems , 1992, 1992 American Control Conference.

[6]  H. Choi,et al.  LMI-based sliding-mode observer design method , 2005 .

[7]  Weihai Zhang,et al.  Control for Discrete-Time Stochastic Systems , 2006 .

[8]  Yulin Huang,et al.  Stochastic H2/Hinfinity control for discrete-time systems with state and disturbance dependent noise , 2007, Autom..

[9]  P. Pagilla,et al.  Controller and observer design for Lipschitz nonlinear systems , 2004, Proceedings of the 2004 American Control Conference.

[10]  David Q. Mayne,et al.  Constrained state estimation for nonlinear discrete-time systems: stability and moving horizon approximations , 2003, IEEE Trans. Autom. Control..

[11]  Giorgio Battistelli,et al.  Moving-horizon state estimation for nonlinear discrete-time systems: New stability results and approximation schemes , 2008, Autom..

[12]  Ali Zemouche Sur l'observation de l'état des systèmes dynamiques non linéaires , 2007 .

[13]  Bertrand Grandvallet,et al.  A software based approach for autonomous projectile attitude and position estimation , 2008, CSTST.

[14]  Chung Seop Jeong,et al.  An LMI approach to discrete-time observer design with stochastic resilience , 2006 .

[15]  A. Alessandri Design of observers for lipschitz nonlinear systems using LMI , 2004 .

[16]  Konrad Reif,et al.  The extended Kalman filter as an exponential observer for nonlinear systems , 1999, IEEE Trans. Signal Process..