Application of a Self-recurrent Wavelet Neural Network in the Modeling and Control of an AC Servo System

To control the nonlinearity, widespread variations in loads and time varying characteristic of the high power ac servo system, the modeling and control techniques are studied here. A self-recurrent wavelet neural network (SRWNN) modeling scheme is proposed, which successfully addresses the issue of the traditional wavelet neural network easily falling into local optimum, and significantly improves the network approximation capability and convergence rate. The control scheme of a SRWNN based on fuzzy compensation is expected. Gradient information is provided in real time for the controller by using a SRWNN identifier, so as to ensure that the learning and adjusting function of the controller of the SRWNN operate well, and fuzzy compensation control is applied to improve rapidity and accuracy of the entire system. Then the Lyapunov function is utilized to judge the stability of the system. The experimental analysis and comparisons with other modeling and control methods, it is clearly shown that the validities of the proposed modeling scheme and control scheme are effective. Copyright © 2014 IFSA Publishing, S. L.

[1]  Faa-Jeng Lin,et al.  Hybrid controller with recurrent neural network for magnetic levitation system , 2005, IEEE Transactions on Magnetics.

[2]  Peter Tino,et al.  IEEE Transactions on Neural Networks , 2009 .

[3]  Sung Jin Yoo,et al.  Stable Predictive Control of Chaotic Systems Using Self-Recurrent Wavelet Neural Network , 2005 .

[4]  Jin Bae Park,et al.  Indirect adaptive control of nonlinear dynamic systems using self recurrent wavelet neural networks via adaptive learning rates , 2007, Inf. Sci..

[5]  Zhidong Qi,et al.  Neural networks modeling of MCFC system and fuzzy control research based on FGA , 2004, Fifth World Congress on Intelligent Control and Automation (IEEE Cat. No.04EX788).

[6]  Le Gui-gao.  Optimal Sliding Mode Servo Control with Integral Term for Rocket Launcher , 2007 .

[7]  Qiang Gao,et al.  A novel active disturbance rejection-based control strategy for a gun control system , 2012 .

[8]  Cao Guang-yi.  Voltage Model of DMFC Based on Neural Networks Identification , 2005 .

[9]  Ji Li-jun.  Modeling and Control for Mine Sweeping Plough Electro-hydraulic Servo System of Certain Mine-clearing Vehicle , 2012 .

[10]  Leang-San Shieh,et al.  A new approach for neural control of nonlinear discrete dynamic systems , 2005, Inf. Sci..

[11]  Xiaoou Li,et al.  Dynamic system identification via recurrent multilayer perceptron , 2002, Inf. Sci..

[12]  P.-H. Shen,et al.  An Adaptive Recurrent-Neural-Network Motion Controller for X-Y Table in CNC Machine , 2006, IEEE Trans. Syst. Man Cybern. Part B.

[13]  Faa-Jeng Lin,et al.  Hybrid controller with recurrent neural network for magnetic levitation system , 2005 .

[14]  Hong Chen,et al.  Universal approximation to nonlinear operators by neural networks with arbitrary activation functions and its application to dynamical systems , 1995, IEEE Trans. Neural Networks.

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

[16]  Wu Zhi Qiao,et al.  A rule self-regulating fuzzy controller , 1992 .

[17]  Kok Kiong Tan,et al.  Nonlinear adaptive control of interconnected systems using neural networks , 2006, IEEE Transactions on Neural Networks.

[18]  Li Jun.  Application of Adaptive Control in Hydraulic Simulator Based on Fuzzy Compensator , 2003 .

[19]  Tommy W. S. Chow,et al.  A recurrent neural-network-based real-time learning control strategy applying to nonlinear systems with unknown dynamics , 1998, IEEE Trans. Ind. Electron..