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工程设计学报
Chinese Journal of Engineering Design  2015, Vol. 22 Issue (2): 161-165    DOI: 10.3785/j.issn. 1006-754X.2015.02.010
    
Companion nonlinear system control based on adaptive RBF network compensation
ZHONG Bin
Equipment Engineering College， Engineering University of Chinese Armed Police Force， Xi′an 710086， China
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Abstract  In order to counteract the nonlinear term in companion nonlinear system, a controller can be designed to precisely linearize the nonlinear system. Generally, there are uncertain factors existing outside the system which lead to the system model uncertainty, so the controller cannot be designed directly. The uncertain term in the system model was adaptively identified by using the principle of that RBF neural network could approximate any continuous function with any precision. The identified result was provided to the controller and it realized the adaptive compensation control of the companion nonlinear system based on neural network. The designed controller was used to control swing angle subsystem of crane-load system. Experiment results showed that the swing angle of the load and the angular velocity of the swing angle were well controlled in about 5 s, and the approximation error of the uncertain term in the system model could reach zero at about 5 s; the designed controller had strong robustness against the uncertain factors of the system and the change of the system parameters.

Key words： companion system      nonlinear control      RBF neural network      adaptive compensation      crane-load system     
Received: 18 March 2014      Published: 28 April 2015
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Cite this article:

ZHONG Bin. Companion nonlinear system control based on adaptive RBF network compensation. Chinese Journal of Engineering Design, 2015, 22(2): 161-165.

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https://www.zjujournals.com/gcsjxb/10.3785/j.issn. 1006-754X.2015.02.010     OR     https://www.zjujournals.com/gcsjxb/Y2015/V22/I2/161


伴随型非线性系统的自适应RBF神经网络补偿控制

为了抵消伴随型非线性系统中的非线性项，可以设计控制器对非线性系统精确线性化．通常由于系统中存在外界不确定性因素导致系统模型的不确定，而不能直接设计控制器．利用“RBF神经网络能以任意精度逼近连续函数”的原理，对系统模型中的不确定项进行自适应辨识，并将辨识结果提供给控制器，从而实现伴随型非线性系统的神经网络自适应补偿控制．将控制器应用于起重机吊重摆角子系统，对摆角进行控制.实验结果表明：吊重摆角及其角速度约在5 s后，得到了很好的控制，并且控制器对系统模型的不确定项的逼近误差约在5 s时达到0；控制器对系统的不确定性因素和系统参数变化均具有很强的鲁棒性．

关键词： 伴随型系统,  非线性系统,  RBF神经网络,  自适应补偿,  起重机吊重系统 
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