Induction motor identification using dynamic two-time scales neural networks with sliding mode learning

This paper presents a novel identification method of induction motor via Dynamic Neural Networks with two-time scales using sliding mode learning. Due to the fast adaptation and superb learning capability, Dynamic Neural Networks with two-time scales using sliding mode learning are used to identify the induction motor including the aspects of fast and slow phenomenon. The sliding mode technique and singularly perturbed theories are used to develop the on-line update laws for dynamic neural networks weights. The global convergence of the identification error to zero is analyzed by means of the Lyapunov function. Simulation results are presented confirming the validity of the above approach.

[1]  Alexander S. Poznyak,et al.  Robust identification by dynamic neural networks using sliding mode learning , 1998 .

[2]  K. Narendra,et al.  A New Adaptive Law for Robust Adaptation without Persistent Excitation , 1986, 1986 American Control Conference.

[3]  Chen-Chung Liu,et al.  ADAPTIVE CONTROL OF NONLINEAR CONTINUOUS-TIME SYSTEMS USING NEURAL NETWORKS - GENERAL RELATIVE DEGREE AND MIMO CASES , 1993 .

[4]  Ronald G. Harley,et al.  Identification and control of induction machines using artificial neural networks , 1993 .

[5]  Sergey Edward Lyshevski,et al.  Identification of induction motor parameters , 1998, Proceedings of the 37th IEEE Conference on Decision and Control (Cat. No.98CH36171).

[6]  Vadim N. Biktashev,et al.  The asymptotic Structure of the Hodgkin-huxley Equations , 2003, Int. J. Bifurc. Chaos.

[7]  Fu-Chuang Chen,et al.  Adaptive control of non-linear continuous-time systems using neural networks—general relative degree and MIMO cases , 1993 .

[8]  Yassine Koubaa.  Recursive identification of induction motor parameters , 2004, Simul. Model. Pract. Theory.

[9]  J J Hopfield,et al.  Neurons with graded response have collective computational properties like those of two-state neurons. , 1984, Proceedings of the National Academy of Sciences of the United States of America.

[10]  Barry W. Williams,et al.  Modeling and simulation of induction machine vector control with rotor resistance identification , 1997 .

[11]  Lennart Ljung,et al.  On-line identification and adaptive trajectory tracking for nonlinear stochastic continuous time systems using differential neural networks , 2001, Autom..

[12]  Frank L. Lewis,et al.  Multilayer neural-net robot controller with guaranteed tracking performance , 1996, IEEE Trans. Neural Networks.

[13]  Ronald G. Harley,et al.  Implementation of a neural network to adaptively identify and control VSI fed induction motor stator currents , 1994, Proceedings of 1994 IEEE Industry Applications Society Annual Meeting.

[14]  Antônio de Pádua Braga,et al.  Sliding mode algorithm for training multilayer artificial neural networks , 1998 .

[15]  Okyay Kaynak,et al.  Sliding Mode Algorithm for Online Learning in Analog Multilayer Feedforward Neural Networks , 2003, ICANN.

[16]  Weidong Luo,et al.  Nonlinear systems identification using dynamic multi-time scale neural networks , 2011, Neurocomputing.

[17]  Manolis A. Christodoulou,et al.  Adaptive control of unknown plants using dynamical neural networks , 1994, IEEE Trans. Syst. Man Cybern..

[18]  Wen-Fang Xie,et al.  A novel sliding-mode control of induction motor using space vector modulation technique. , 2005, ISA transactions.

[19]  Vadim I. Utkin,et al.  Sliding Modes in Control and Optimization , 1992, Communications and Control Engineering Series.

[20]  Alexander S. Poznyak,et al.  Indirect adaptive control via parallel dynamic neural networks , 1999 .

[21]  Xuan Han,et al.  Nonlinear systems identification using dynamic multi-time scales neural networks , 2008, 2008 IEEE International Conference on Automation Science and Engineering.

[22]  Alexander S. Poznyak,et al.  Robust identification of uncertain nonlinear systems with state constrains by Differential Neural Networks , 2009, 2009 International Joint Conference on Neural Networks.

[23]  Arthur Albert,et al.  Regression and the Moore-Penrose Pseudoinverse , 2012 .

[24]  Eliezer Colina-Morles,et al.  A sliding mode strategy for adaptive learning in Adalines , 1995 .

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

[26]  Ahmad B. Rad,et al.  Identification and control of continuous-time nonlinear systems via dynamic neural networks , 2003, IEEE Trans. Ind. Electron..

[27]  Lennart Ljung,et al.  System Identification: Theory for the User , 1987 .

[28]  Hassan K. Khalil,et al.  Singular perturbation methods in control : analysis and design , 1986 .

[29]  Xiaoou Li,et al.  Some new results on system identification with dynamic neural networks , 2001, IEEE Trans. Neural Networks.

[30]  Namho Hur,et al.  A real-time parameter identification scheme for the sensorless control of induction motors using a reduced order model , 1996, Proceedings of the 1996 IEEE IECON. 22nd International Conference on Industrial Electronics, Control, and Instrumentation.