Abstract The position tracking accuracy of permanent magnet linear synchronous motor (PMLSM) servo system is susceptible to parameter variation, load disturbance, friction and other uncertain factors. Therefore, a backstepping terminal sliding mode control method based on radial basis function (RBF) neural network is proposed. Firstly, the PMLSM dynamic mathematical model with uncertainty is established. Then, the state of the system converges to the equilibrium point in a finite time and the response speed of the system is improved by using the backstepping terminal sliding mode control. In order to further weaken chattering phenomenon, the saturation function is designed combined the hyperbolic tangent function with boundary layer thickness to replace the signum function. Moreover, RBF neural network is used to approximate the uncertainties in the system, and then fast-tracking performance and strong immunity ability are obtained. Finally, the experimental results show that the proposed control method not only improves the tracking and robustness of the system, but also significantly weakens the chattering problem.
Fu Dongxue,Zhao Ximei. Backstepping Terminal Sliding Mode Control Based on Radial Basis Function Neural Network for Permanent Magnet Linear Synchronous Motor[J]. Transactions of China Electrotechnical Society, 2020, 35(12): 2545-2553.
[1] Yang Xiaojun, Lu Dun, Zhang Jun, et al.Dynamic electromechanical coupling resulting from the air-gap fluctuation of the linear motor in machine tools[J]. International Journal of Machine Tools & Manufacture, 2015, 94: 100-108. [2] 智淑亚, 吴洪兵. 数控进给伺服系统摩擦补偿控制仿真[J]. 沈阳工业大学学报, 2019, 41(4): 361-365. Zhi Shuya, Wu Hongbing.Simulation of friction compensation control of NC feed servo system[J]. Journal of Shenyang University of Technology, 2019, 41(4): 361-365. [3] 朱国昕, 雷鸣凯, 赵希梅. 永磁同步电机伺服系统自适应迭代学习控制[J]. 沈阳工业大学学报, 2018, 40(1): 6-11. Zhu Guoxin, Lei Mingkai, Zhao Ximei.Adaptive iterative learning control for permanent magnet synchronous motor servo system[J]. Journal of Shenyang University of Technology, 2018, 40(1): 6-11. [4] 赵希梅, 原浩, 朱文彬. 基于小波神经网络和非线性扰动观测器的直线伺服系统控制[J]. 电工技术学报, 2019, 34(19): 3989-3996. Zhao Ximei, Yuan Hao, Zhu Wenbin.Control of linear servo system based on wavelet neural network and nonlinear disturbance observer[J]. Transactions of China Electrotechnical Society, 2019, 34(19): 3989-3996. [5] 原浩, 赵希梅. 基于积分滑模的永磁直线同步电动机直接推力控制[J]. 电工技术学报, 2019, 34(3): 483-488. Yuan Hao, Zhao Ximei.Direct thrust force control based on integral sliding mode for permanent magnet linear synchronous motor[J]. Transactions of China Electrotechnical Society, 2019, 34(3): 483-488. [6] 张博, 齐蓉, 林辉. 激光切割永磁直线伺服系统的反演滑模控制[J]. 电工技术学报, 2018, 33(3): 642-651. Zhang Bo, Qi Rong, Lin Hui.Back-stepping sliding mode control of laser cutting permanent magnet linear servo control system[J]. Transactions of China Electrotechnical Society, 2018, 33(3): 642-651. [7] Gao Jianguo, Liu Yuchao, Zhou Jun.Integral terminal sliding mode control for nonlinear systems[J]. Journal of Systems Engineering and Electronics, 2018, 29(3): 571-579. [8] Nadda S, Swarup A.Development of backstepping based sliding mode control for a quadrotor[C]//IEEE 10th International Colloquium on Signal Processing & ITS Applications, Kuala Lumpur, Malaysia, 2014: 10-13. [9] Guan Lirong, Gong Min, Sun Shengbing.Adaptive backstepping sliding mode robust tracking control for permanent magnet linear synchronous motor[J]. Electric Drive, 2009, 15(2): 124-133. [10] Tran M D, Kang H J.A novel adaptive finite-time tracking control for robotic manipulators using nonsingular terminal sliding mode and RBF neural networks[J]. International Journal of Precision Engineering and Manufacturing, 2016, 17(7): 863-870. [11] Liang Bo, Zhu Yuqing, Li Yuren, et al.Adaptive nonsingular fast terminal sliding mode control for braking systems with electro-mechanical actuators based on radial basis function[J]. Energies, 2017, 10(10): 1637-1651.
2020, Vol. 35 
