Networked predictive control for Hammerstein systems

Thispaperisconcernedwith theproblemsofdesignandstabilityanalysis of networked predictive control for Hammerstein systems. The Hammerstein nonlinearityis removed(orpartiallyremoved)byinvertingit. By predictingthe future control sequence, the random network-induced delay and data dropout are compensated actively. The stability of the closed-loop system is analyzed by applying the switched Lyapunov function approach. Simulation results are presented to illustrate the validity of the proposed method.

[1]  Wei Zhang,et al.  Stability of networked control systems , 2001 .

[2]  Yuanqing Xia,et al.  Design and Stability Criteria of Networked Predictive Control Systems With Random Network Delay in the Feedback Channel , 2007, IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews).

[3]  Panos J. Antsaklis,et al.  On the model-based control of networked systems , 2003, Autom..

[4]  Guo-Ping Liu,et al.  Networked Predictive Control Systems Based on the Hammerstein Model , 2008, IEEE Transactions on Circuits and Systems II: Express Briefs.

[5]  Ding Bao Stability Analysis of Generalized Predictive Control with Input Nonlinearity Based-on Popov′s Theorem , 2003 .

[6]  Jamal Daafouz,et al.  Stability analysis and control synthesis for switched systems: a switched Lyapunov function approach , 2002, IEEE Trans. Autom. Control..

[7]  Dong Yue,et al.  Network-based robust H ∞ control of systemswith uncertainty , 2005 .

[8]  João Pedro Hespanha,et al.  A Survey of Recent Results in Networked Control Systems , 2007, Proceedings of the IEEE.

[9]  Guang-Hong Yang,et al.  Network‐based robust H∞ control of continuous‐time systems with uncertainty , 2009 .

[10]  Linda Bushnell,et al.  Asymptotic behavior of nonlinear networked control systems , 2001, IEEE Trans. Autom. Control..

[11]  A. Palazoglu,et al.  Nolinear model predictive control using Hammerstein models , 1997 .

[12]  Asok Ray,et al.  Integrated Communication and Control Systems: Part I—Analysis , 1988 .

[13]  Long Wang,et al.  Sampled-data stabilisation of networked control systems with nonlinearity , 2005 .

[14]  Mats Viberg,et al.  Compensation for Nonlinearity in a Hammerstein System Using the Coherence Function With Application to Nonlinear Acoustic Echo Cancellation , 2007, IEEE Transactions on Signal Processing.

[15]  Senchun Chai,et al.  Design and stability analysis of networked control systems with random communication time delay using the modified MPC , 2006 .

[16]  Robert E. Kearney,et al.  A least-squares parameter estimation algorithm for switched Hammerstein systems with applications to the VOR , 2005, IEEE Transactions on Biomedical Engineering.

[17]  Johan A. K. Suykens,et al.  Subspace identification of Hammerstein systems using least squares support vector machines , 2005, IEEE Transactions on Automatic Control.

[18]  Luca Sani,et al.  Automatic nonlinear auto-tuning method for Hammerstein modeling of electrical drives , 2001, IEEE Trans. Ind. Electron..

[19]  Huaping Liu,et al.  Predictive observer‐based control for networked control systems with network‐induced delay and packet dropout , 2008 .

[20]  Hai Lin,et al.  Stabilization and performance analysis for a class of switched systems , 2004, 2004 43rd IEEE Conference on Decision and Control (CDC) (IEEE Cat. No.04CH37601).

[21]  Guo-Ping Liu,et al.  A Predictive Control-Based Approach to Networked Hammerstein Systems: Design and Stability Analysis , 2008, IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics).

[22]  Xian-Ming Tang,et al.  Feedback scheduling of model-based networked control systems with flexible workload , 2008, Int. J. Autom. Comput..

[23]  James Moyne,et al.  Analysis and modeling of networked control systems: MIMO case with multiple time delays , 2001, Proceedings of the 2001 American Control Conference. (Cat. No.01CH37148).

[24]  Francisco Jurado,et al.  Predictive control of solid oxide fuel cells using fuzzy Hammerstein models , 2006 .

[25]  Jun Ren,et al.  Linearizing control of induction motor based on networked control systems , 2009, Int. J. Autom. Comput..