Adaptive minimum variance control for stochastic fuzzy T-S ARMAX model

Adaptive minimum variance control for stochastic T-S fuzzy ARMAX model is addressed in this study. From the fuzzy ARMAX model, a fuzzy one-step ahead prediction model is first introduced. A stochastic gradient algorithm is then proposed to identify the parameters of the related one-step-ahead predictor. Under the direct adaptive control scheme, minimum variance control is applied to find the control law to make the output track a desired reference signal. Stability and performance of the adaptive stochastic fuzzy control system are rigorously derived. Simulation study is also made to verify the developed results.

[1]  Kiyotaka Izumi,et al.  Stochastic fuzzy servo control using multiple linear dynamic models , 1998, 1998 Second International Conference. Knowledge-Based Intelligent Electronic Systems. Proceedings KES'98 (Cat. No.98EX111).

[2]  C.S.G. Lee,et al.  Fuzzy model-reference adaptive control , 1995, IEEE Trans. Syst. Man Cybern..

[3]  T. T. Ho.  Stochastic fuzzy direct adaptive control , 1994, Proceedings of 1994 IEEE 3rd International Fuzzy Systems Conference.

[4]  M. Sugeno,et al.  Structure identification of fuzzy model , 1988 .

[5]  Bor-Sen Chen,et al.  H∞ tracking design of uncertain nonlinear SISO systems: adaptive fuzzy approach , 1996, IEEE Trans. Fuzzy Syst..

[6]  Michio Sugeno,et al.  A fuzzy-logic-based approach to qualitative modeling , 1993, IEEE Trans. Fuzzy Syst..

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

[8]  Hong-Tzer Yang,et al.  A new short-term load forecasting approach using self-organizing fuzzy ARMAX models , 1998 .

[9]  Jinglu Hu,et al.  Nonlinear model predictive control utilizing a neuro-fuzzy predictor , 2000, Smc 2000 conference proceedings. 2000 ieee international conference on systems, man and cybernetics. 'cybernetics evolving to systems, humans, organizations, and their complex interactions' (cat. no.0.

[10]  Keigo Watanabe.  Stochastic fuzzy control. I. Theoretical derivation , 1995, Proceedings of 1995 IEEE International Conference on Fuzzy Systems..

[11]  Li-Xin Wang.  Stable adaptive fuzzy control of nonlinear systems , 1993, IEEE Trans. Fuzzy Syst..

[12]  Bore-Kuen Lee,et al.  Optimal tracking design for stochastic fuzzy systems , 2003, IEEE Trans. Fuzzy Syst..

[13]  Jinglu Hu,et al.  A hybrid quasi-ARMAX modeling scheme for identification and control of nonlinear systems , 1996, Proceedings of 35th IEEE Conference on Decision and Control.

[14]  Li-Xin Wang.  Modeling and control of hierarchical systems with fuzzy systems , 1997, Autom..

[15]  Giorgio Rizzoni,et al.  Fault detection and isolation for an experimental internal combustion engine via fuzzy identification , 1995, IEEE Trans. Control. Syst. Technol..