Parameters Turning of the Active-Disturbance Rejection Controller Based on RBF Neural Network

Abstract : The Active-disturbance rejection control (ADRC) has the advantage of strong robustness, anti-interference capability, and it does not rely on the accurate math model of controlled plant. But the parameter self-turning of ADRC isn’t as easy as PID controller because there are more parameters to turn in ADRC. In this paper the parameters are self-turning by the Radial Basis Function (RBF) Neural Network. The results of the simulation indicate that the controller has good anti-interference capability and fast response. The robustness of the system is improved.
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Communication dans un congrès
Daoliang Li; Yande Liu; Yingyi Chen. 4th Conference on Computer and Computing Technologies in Agriculture (CCTA), Oct 2010, Nanchang, China. Springer, IFIP Advances in Information and Communication Technology, AICT-347 (Part IV), pp.260-267, 2011, Computer and Computing Technologies in Agriculture IV. 〈10.1007/978-3-642-18369-0_30〉
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Baifen Liu, Ying Gao. Parameters Turning of the Active-Disturbance Rejection Controller Based on RBF Neural Network. Daoliang Li; Yande Liu; Yingyi Chen. 4th Conference on Computer and Computing Technologies in Agriculture (CCTA), Oct 2010, Nanchang, China. Springer, IFIP Advances in Information and Communication Technology, AICT-347 (Part IV), pp.260-267, 2011, Computer and Computing Technologies in Agriculture IV. 〈10.1007/978-3-642-18369-0_30〉. 〈hal-01564845〉

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