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Backstepping Control of PMSM Based on RBF Neural Network

机译：基于RBF神经网络的PMSM反步控制。

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摘要

Based on PMSM dynamics and nonlinear load characteristics, a new nonlinear speed controller is designed with vector control scheme. The proposed controller was composed of backstepping speed controller and error regulator based on RBF neural network. The former was designed to ensure a desired speed tracking control, and the later was derived to realize the robust adaptive control against load torque variations. A second-order filter is adopted to reduce the speed overshoot in the starting course of PMSM. Backstepping control system of PMSM based on RBF neural network was established in Simulink. With dSPACE system and the external drive circuit, the completed control system hardware-in-loop real-time simulation was achieved successfully. Simulation and experimental results show the backstepping control system of PMSM based on RBF neural network Given in this paper can remain its good speed dynamic tracking performance and strong robustness when load torque disturbances appeared.
机译：基于永磁同步电机的动力学特性和非线性负载特性，设计了一种新型的带有矢量控制方案的非线性速度控制器。所提出的控制器由基于RBF神经网络的反步速度控制器和误差调节器组成。前者旨在确保所需的速度跟踪控制，而后者则旨在实现针对负载转矩变化的鲁棒自适应控制。在PMSM的启动过程中，采用了二阶滤波器来减小速度过冲。在Simulink中建立了基于RBF神经网络的PMSM逆推控制系统。借助dSPACE系统和外部驱动电路，成功完成了完整的控制系统硬件在环实时仿真。仿真和实验结果表明，基于RBF神经网络的PMSM逆推控制系统在出现负载转矩扰动时可以保持良好的速度动态跟踪性能和较强的鲁棒性。

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