RBF Network Adaptive Sliding Mode Control of Ball and Plate System Based on Reaching Law

被引:0
|
作者
Jiang-Feng Li
Feng-Hong Xiang
机构
[1] Kunming University of Science and Technology,Faculty of Information Engineering and Automation
关键词
A novel double-power reaching law; Ball and plate system; Minimum parameter learning method; Response speed; RBF neural network; Sliding mode control; Weakening chattering;
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中图分类号
学科分类号
摘要
Aiming at the slow approach speed and chattering problems of sliding mode variable structure control of the ball and plate system, a Radial Basis Function (RBF) network adaptive control scheme based on the reaching law is proposed. Firstly, a double sliding mode surface is defined, and the dual-speed self-adjusting approach parameters are designed on the basis of the power reaching law; secondly, the RBF neural network is used to approximate the unknown part of the nonlinear model to reduce the fuzzy gain. At the same time, the estimated value of the parameter is employed to replace the neural network weight adjustment, which makes the adaptive algorithm simpler. Finally, Lyapunov theory is used to prove the stability of the controller, and the effectiveness of the controller is verified by simulation.
引用
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页码:9393 / 9404
页数:11
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