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Fuzzy Rules Emulated Network Adaptive Controller with Unfixed Learning Rate for a Class of Unknown Discrete-time Nonlinear Systems

Authors: Chidentree Treesatayapun


A direct adaptive controller for a class of unknown nonlinear discrete-time systems is presented in this article. The proposed controller is constructed by fuzzy rules emulated network (FREN). With its simple structure, the human knowledge about the plant is transferred to be if-then rules for setting the network. These adjustable parameters inside FREN are tuned by the learning mechanism with time varying step size or learning rate. The variation of learning rate is introduced by main theorem to improve the system performance and stabilization. Furthermore, the boundary of adjustable parameters is guaranteed through the on-line learning and membership functions properties. The validation of the theoretical findings is represented by some illustrated examples.

Keywords: learning algorithm, neuro-fuzzy, nonlinear discrete time

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[1] J. Zhao and I. Kanellakopoulos, "Active Identification for discretetime nonlinear control-part I: Output-Feedback systems," IEEE Trans. Automat. Contr., vol. 47, no. 2, pp. 210-224, Feb. 2002.
[2] J. Zhao and I. Kanellakopoulos, "Active Identification for discrete-time nonlinear control-part II: Strict-Feedback systems," IEEE Trans. Automat. Contr., vol. 47, no. 2, pp. 225-240, Feb. 2002.
[3] H. Deng, H.X. Li and Y.H. Wu, "Feedback-Linearization-Based neural adaptive control for unknown nonaffine nonlinear discrete-time systems," IEEE Trans. Neural Networks, vol. 19, no. 9, pp. 1615-1625, Sep. 2008.
[4] B.T. Thumati and S. Jagannathan, "Neural network control of a class of nonlinear discrete time systems with asymptotic stability guarantees," American control conference 2009, St. Louis, MO, USA, pp. 2934-2939, 10-12 Jun. 2009.
[5] H. E. Psillakis, "Sampled-Data adaptive NN tracking control of uncertain nonlinear systems," IEEE Trans. Neural Networks, vol. 20, no. 2, pp. 336-355, Feb. 2009.
[6] C.T. Lin, Neural fuzzy systems, Prentice-Hall, 1996.
[7] S. Jagannathan, Neural Network Control of Nonlinear Discrete-Time Systems, Boca Raton, FL: Taylor & Francis, 2006.