A fuzzy-neural multi-model for nonlinear systems identification and control

The paper proposed to apply a hierarchical fuzzy-neural multi-model and Takagi-Sugeno (T-S) rules with recurrent neural procedural consequent part for systems identification, states estimation and adaptive control of complex nonlinear plants. The parameters and states of the local recurrent neural network models are used for a local direct and indirect adaptive trajectory tracking control systems design. The designed local control laws are coordinated by a fuzzy rule-based control system. The upper level defuzzyfication is performed by a recurrent neural network. The applicability of the proposed intelligent control system is confirmed by simulation examples and by a DC-motor identification and control experimental results. Two main cases of a reference and plant output fuzzyfication are considered-a two membership functions without overlapping and a three membership functions with overlapping. In both cases a good convergent results are obtained.

[1]  Andreas Nürnberger,et al.  A hierarchical recurrent neuro-fuzzy model for system identification , 2003, Int. J. Approx. Reason..

[2]  R. Garrido,et al.  A fuzzy neural recurrent multi-model for systems identification and control , 2001, 2001 European Control Conference (ECC).

[3]  M. E. Ahmed.  Block partial derivative and its application to neural-net-based direct model-reference adaptive control , 1994 .

[4]  Marcelo C. M. Teixeira,et al.  Stabilizing controller design for uncertain nonlinear systems using fuzzy models , 1999, IEEE Trans. Fuzzy Syst..

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

[6]  E. Gortcheva,et al.  Fuzzy-Neural Model for Nonlinear Systems Identification 1 , 1998 .

[7]  Giovanni Soda,et al.  Local Feedback Multilayered Networks , 1992, Neural Computation.

[8]  Jyh-Shing Roger Jang,et al.  ANFIS: adaptive-network-based fuzzy inference system , 1993, IEEE Trans. Syst. Man Cybern..

[9]  Rudolf Kruse,et al.  Using recurrent neuro-fuzzy techniques for the identification and simulation of dynamic systems , 2001, Neurocomputing.

[10]  Ioannis B. Theocharis.  A high-order recurrent neuro-fuzzy system with internal dynamics: Application to the adaptive noise cancellation , 2006, Fuzzy Sets Syst..

[11]  Simon Haykin,et al.  Neural networks , 1994 .

[12]  Jong-Hwan Kim,et al.  Robust adaptive stick-slip friction compensation , 1995, IEEE Trans. Ind. Electron..

[13]  C. S. George Lee,et al.  Neural fuzzy systems: a neuro-fuzzy synergism to intelligent systems , 1996 .

[14]  Jürgen Adamy,et al.  Equilibria of recurrent fuzzy systems , 2003, Fuzzy Sets Syst..

[15]  Peter J. Gawthrop,et al.  Neural networks for control systems - A survey , 1992, Autom..

[16]  Cheng-Jian Lin,et al.  A compensation-based recurrent fuzzy neural network for dynamic system identification , 2006, Eur. J. Oper. Res..

[17]  Lida Xu,et al.  A new type of recurrent fuzzy neural network for modeling dynamic systems , 2001, Knowl. Based Syst..

[18]  Simon Haykin,et al.  Neural Networks: A Comprehensive Foundation , 1998 .

[19]  Marzuki Khalid,et al.  Neuro-control and its applications , 1996 .

[20]  Paris A. Mastorocostas,et al.  A recurrent fuzzy-neural model for dynamic system identification , 2002, IEEE Trans. Syst. Man Cybern. Part B.

[21]  Eric A. Wan,et al.  Diagrammatic Derivation of Gradient Algorithms for Neural Networks , 1996, Neural Computation.

[22]  Cheng-Jian Lin,et al.  Identification and prediction using recurrent compensatory neuro-fuzzy systems , 2005, Fuzzy Sets Syst..

[23]  Yoh-Han Pao,et al.  A recurrent neural net approach to one-step ahead control problems , 1994, IEEE Trans. Syst. Man Cybern..

[24]  Federico Thomas,et al.  A hybrid multimodel neural network for nonlinear systems identification , 1999, IJCNN'99. International Joint Conference on Neural Networks. Proceedings (Cat. No.99CH36339).

[25]  Meng Joo Er,et al.  NARMAX time series model prediction: feedforward and recurrent fuzzy neural network approaches , 2005, Fuzzy Sets Syst..

[26]  I.S. Baruch,et al.  An advanced neural network topology and learning, applied for identification and control of a DC motor , 2002, Proceedings First International IEEE Symposium Intelligent Systems.

[27]  Kumpati S. Narendra,et al.  Identification and control of dynamical systems using neural networks , 1990, IEEE Trans. Neural Networks.

[28]  Robert Babuska,et al.  Fuzzy Modeling for Control , 1998 .

[29]  Ieroham S. Baruch,et al.  Adaptive discrete - time sliding mode control using recurrent neural identifier , 2004 .

[30]  Federico Thomas,et al.  Adaptive Neural Control of Nonlinear Systems , 2001, ICANN.

[31]  Thierry-Marie Guerra,et al.  Conditions of output stabilization for nonlinear models in the Takagi-Sugeno's form , 2006, Fuzzy Sets Syst..

[32]  Paris A. Mastorocostas,et al.  An orthogonal least-squares method for recurrent fuzzy-neural modeling , 2003, Fuzzy Sets Syst..

[33]  Jürgen Adamy,et al.  Regularity and chaos in recurrent fuzzy systems , 2003, Fuzzy Sets Syst..

[34]  P. S. Sastry,et al.  Memory neuron networks for identification and control of dynamical systems , 1994, IEEE Trans. Neural Networks.