NONLINEAR MODELING AND CONTROLLING OF ARTIFICIAL MUSCLE SYSTEM USING NEURAL NETWORKS

The pneumatic artificial muscles are widely used in the fields of medical robots,etc.Neural networks are applied to modeling and controlling of artificial muscle system.A single-joint artificial muscle test system is designed.The recursive prediction error (RPE) algorithm which yields faster convergence than back propagation (BP) algorithm is applied to train the neural networks.The realization of RPE algorithm is given.The difference of modeling of artificial muscles using neural networks with different input nodes and different hidden layer nodes is discussed.On this basis the nonlinear control scheme using neural networks for artificial muscle system has been introduced.The experimental results show that the nonlinear control scheme yields faster response and higher control accuracy than the traditional linear control scheme.

作 者： Tian Sheping Ding Guoqing Yan Detian Lin Liangming   作者單位： Department of Information Measurement and Instrumentation,Shanghai Jiaotong University,Shanghai 200030,China  刊 名： 機(jī)械工程學(xué)報(bào)(英文版)  EI 英文刊名： CHINESE JOURNAL OF MECHANICAL ENGINEERING  年，卷(期)： 2004 17(2)  分類號(hào)：   關(guān)鍵詞： Artificial muscle Neural networks Recursive prediction error algorithm Nonlinear modeling and controlling  

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