RBF neural network control for linear motor-direct drive actuator based on an extended state observer.

*(English)*Zbl 1368.93662Summary: Hydraulic power and other kinds of disturbance in a Linear Motor-Direct Drive Actuator (LM-DDA) have a great impact on the performance of the system. A mathematical model of the LM-DDA system is established and a double-loop control system is presented. An Extended State Observer (ESO) with switched gain is utilized to estimate the influence of the hydraulic power and other load disturbances. Meanwhile, Radial Basis Function (RBF) neural network is utilized to optimize the parameters in this intelligent controller. The results of the dynamic tests demonstrate the performance with rapid response and improved accuracy could be attained by the proposed control scheme.

##### MSC:

93E03 | Stochastic systems in control theory (general) |

93C85 | Automated systems (robots, etc.) in control theory |

92B20 | Neural networks for/in biological studies, artificial life and related topics |

93B07 | Observability |

93E10 | Estimation and detection in stochastic control theory |

93C05 | Linear systems in control theory |

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\textit{Z. Liu} and \textit{T. Chen}, Discrete Dyn. Nat. Soc. 2016, Article ID 8390529, 10 p. (2016; Zbl 1368.93662)

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