Direct model reference adaptive controller based-on neural-fuzzy techniques for nonlinear dynamical systems

This paper presents a direct neural-fuzzy-based Model Reference Adaptive Controller (MRAC) for nonlinear dynamical systems with unknown parameters. The two-phase learning is implemented to perform structure identification and parameter estimation for the controller. In the first phase, similarity index-based fuzzy c-means clustering technique extracts the fuzzy rules in the premise part for the neural-fuzzy controller. This technique enables the recruitment of rule parameters in accordance to the number of clusters and kernel centers it automatically generated. In the second phase, the parameters of the controller are directly tuned from the training data via the tracking error. The consequent parts of the rules are thus determined. This iterative process employs Radial Basis Function Neural Network (RBFNN) structure with a reference model to provide a closed-loop performance feedback.

[1]  Teuvo Kohonen,et al.  The self-organizing map , 1990, Neurocomputing.

[2]  J. M. Skowronski,et al.  Adaptive nonlinear model following and avoidance under uncertainty , 1988 .

[3]  Chuen-Tsai Sun,et al.  Neuro-fuzzy modeling and control , 1995, Proc. IEEE.

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

[5]  Hak-Keung Lam,et al.  On design of a switching controller for nonlinear systems with unknown parameters based on a model reference approach , 2001, 10th IEEE International Conference on Fuzzy Systems. (Cat. No.01CH37297).

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

[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]  John Moody,et al.  Fast Learning in Networks of Locally-Tuned Processing Units , 1989, Neural Computation.

[9]  Derong Liu,et al.  Neural network-based model reference adaptive control system , 2000, IEEE Trans. Syst. Man Cybern. Part B.

[10]  Sadaaki Miyamoto,et al.  An overview and new methods in fuzzy clustering , 1998, 1998 Second International Conference. Knowledge-Based Intelligent Electronic Systems. Proceedings KES'98 (Cat. No.98EX111).

[11]  A. Abran,et al.  Functional Equivalence between Radial Basis Function Neural Networks and Fuzzy Analogy in Software Cost Estimation , 2008, 2008 3rd International Conference on Information and Communication Technologies: From Theory to Applications.

[12]  Derek A. Linkens,et al.  A systematic neuro-fuzzy modeling framework with application to material property prediction , 2001, IEEE Trans. Syst. Man Cybern. Part B.

[13]  Karim Djouani,et al.  Neuro-fuzzy based approach for hybrid force/position robot control , 2004, Integr. Comput. Aided Eng..

[14]  James C. Bezdek,et al.  C-means Clustering with the Ll and L∞ Norms , 1991, IEEE Transactions on Systems, Man and Cybernetics.

[15]  J. Bezdek,et al.  c-means clustering with the l/sub l/ and l/sub infinity / norms , 1991 .

[16]  Jianming Lu,et al.  Application of neural networks to nonlinear adaptive control systems , 2000, WCC 2000 - ICSP 2000. 2000 5th International Conference on Signal Processing Proceedings. 16th World Computer Congress 2000.

[17]  Tong Liu,et al.  Neural network-based model reference adaptive systems for high performance motor drives and motion controls , 2000, Conference Record of the 2000 IEEE Industry Applications Conference. Thirty-Fifth IAS Annual Meeting and World Conference on Industrial Applications of Electrical Energy (Cat. No.00CH37129).