Abstract：The permanent magnet linear synchronous motor (PMLSM) servo system has the uncertainties such as parameter variations, external disturbance and friction. Therefore, a control scheme combining integral backstepping control and adaptive modified Elman neural network was adopted. Firstly, for the nonlinear characteristics of PMLSM servo system, the integral backstepping control method was used to design the virtual control function through the step-by-step modification algorithm to realize the global adjustment and position tracking of the system. Secondly, an adaptive modified Elman neural network was designed to estimate the uncertainty in the system, and the on-line parameter learning law of the neural network was derived by the adaptive law based on Lyapunov function. Thus, the system had the ability to adapt to the time-varying characteristics and overcome the influence of uncertainty on the system, thereby improving the robustness of the system. Finally, the experimental results show that the proposed control scheme is effective, and the tracking performance and robust performance of the system are obviously improved.
赵希梅, 付东学, 金洋洋. 永磁直线伺服系统的自适应改进Elman神经网络积分反推控制[J]. 电工技术学报, 2020, 35(2): 266-273.
Zhao Ximei, Fu Dongxue, Jin Yangyang. Adaptive Modified Elman Neural Network Integral Backstepping Control for Permanent Magnet Linear Servo System. Transactions of China Electrotechnical Society, 2020, 35(2): 266-273.
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2020, Vol. 35 
