文章基本信息

标题：Adaptive RBF Neural Network Control Method for Pneumatic Position Servo System ⁎
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作者：Hai-Peng Ren ; Shan-Shan Jiao ; Xuan Wang 等
期刊名称：IFAC PapersOnLine
印刷版ISSN：2405-8963
出版年度：2020
卷号：53
期号：2
页码：8826-8831
DOI：10.1016/j.ifacol.2020.12.1394
语种：English
出版社：Elsevier
摘要：AbstractWith the development of control theory and the pneumatic element, the application of pneumatic systems has attracted more attention because of the performance to price ratio improvement. Despite of these, there are still challenge to deal with the nonlinearity of the system, the uncertainty of the parameters, the input saturation and the unknown control direction in the tracking control of pneumatic system. In this paper, the nonlinearity and model uncertainty are treated with adaptive radial basis function neural network (RBFNN), meanwhile, the unknown control direction and input saturation are dealt with the Nussbaum function and Gauss error function, respectively. The stability of the designed controller is proved by Lyapunov theory. Finally, the experimental and comparison results show the effectiveness and superiority of the proposed method.
关键词：KeywordsPneumatic systemRBFNNunknown model parametersunknown control directioninput saturation