A new discrete-time sliding-mode control with time-varying gain and neural identification

In this paper, we present a new sliding mode controller for a class of unknown non-linear discrete-time systems. We make the following two modifications. (1) The neural identifier which is used to estimate the unknown non-linear system uses the projection and the dead-zone approaches to assure non-singularity in the controller and stability of identification error. (2) We propose a new sliding mode controller with time-varying gain to reduce chattering. A necessary condition is given to make the switching function decrease exponentially. We prove that the closed-loop system with the sliding mode controller and the neural identifier is stable.

[1]  Charles R. Johnson,et al.  Matrix analysis , 1985, Statistical Inference for Engineers and Data Scientists.

[2]  M. Corless,et al.  Continuous state feedback guaranteeing uniform ultimate boundedness for uncertain dynamic systems , 1981 .

[3]  Anuradha M. Annaswamy,et al.  Robust Adaptive Control , 1984, 1984 American Control Conference.

[4]  R. Fung,et al.  Comparison of sliding-mode and fuzzy neural network control for motor-toggle servomechanism , 1998 .

[5]  Guang-Hong Yang,et al.  Decentralized control of symmetric systems , 2001 .

[6]  Geoffrey E. Hinton,et al.  Learning representations by back-propagating errors , 1986, Nature.

[7]  Heinz Unbehauen,et al.  A new algorithm for discrete-time sliding-mode control using fast output sampling feedback , 2002, IEEE Trans. Ind. Electron..

[8]  Weiping Li,et al.  Applied Nonlinear Control , 1991 .

[9]  Daniel G. Sbarbaro-Hofer,et al.  An adaptive sliding-mode controller for discrete nonlinear systems , 2000, IEEE Trans. Ind. Electron..

[10]  Hung-Yuan Chung,et al.  Neuro-sliding mode control with its applications to seesaw systems , 2001, IEEE Transactions on Neural Networks.

[11]  Ching-Hung Lee,et al.  Identification and control of dynamic systems using recurrent fuzzy neural networks , 2000, IEEE Trans. Fuzzy Syst..

[12]  S. Żak,et al.  On discrete-time variable structure sliding mode control , 1999 .

[13]  Weibing Gao,et al.  Discrete-time variable structure control systems , 1995, IEEE Trans. Ind. Electron..

[14]  Yong Fang,et al.  Use of a recurrent neural network in discrete sliding-mode control , 1999 .

[15]  Richard D. Braatz,et al.  On the "Identification and control of dynamical systems using neural networks" , 1997, IEEE Trans. Neural Networks.

[16]  O. Kaynak,et al.  On the stability of discrete-time sliding mode control systems , 1987 .

[17]  K. Furuta Sliding mode control of a discrete system , 1990 .

[18]  Hazem N. Nounou,et al.  Stable auto-tuning of adaptive fuzzy/neural controllers for nonlinear discrete-time systems , 2004, IEEE Transactions on Fuzzy Systems.

[19]  H. Sira-Ramírez Non-linear discrete variable structure systems in quasi-sliding mode , 1991 .

[20]  Andrzej Bartoszewicz,et al.  Discrete-time quasi-sliding-mode control strategies , 1998, IEEE Trans. Ind. Electron..

[21]  Hassan K. Khalil,et al.  Adaptive control of a class of nonlinear discrete-time systems using neural networks , 1995, IEEE Trans. Autom. Control..

[22]  George Cybenko,et al.  Approximation by superpositions of a sigmoidal function , 1992, Math. Control. Signals Syst..

[23]  Xinghuo Yu,et al.  Discretization behaviors of equivalent control based sliding-mode control systems , 2003, IEEE Trans. Autom. Control..

[24]  Li-Xin Wang,et al.  A Course In Fuzzy Systems and Control , 1996 .

[25]  Sarangapani Jagannathan,et al.  Control of a class of nonlinear discrete-time systems using multilayer neural networks , 2001, IEEE Trans. Neural Networks.

[26]  Murat Dogruel Input linearization of nonlinear systems via pulse-width control , 2003, IEEE Trans. Autom. Control..

[27]  Quanmin Zhu,et al.  Stable adaptive neurocontrol for nonlinear discrete-time systems , 2004, IEEE Trans. Neural Networks.