Parallel Displacement Velocity Control of Permanent Magnet Synchronous Linear Motor with Variable Parameters Based on Composite Neural Network Reconstruction Object
Bao Mingkun, Zhou Yangzhong
Fujian Key Laboratory of New Energy Generation and Power Conversion Fuzhou University Fuzhou 350116 China
Abstract:A permanent magnet synchronous linear servo motor has the characteristics of high thrust density, high efficiency, and fast control response. It is widely used in rail transit, intelligent manufacturing, and aerospace fields. The electrical or mechanical parameters of the motor and nonlinear resistance interference will affect the high precision control of the linear servo. Therefore, the stable and efficient displacement control algorithm has important engineering application value to improve the system performance. Servo control can be divided into serial and parallel systems according to the relative position of displacement and velocity loop. However, whether serial or parallel control, most controller parameters need to be set in advance and are generally fixed, and adjusting the actual control effect is impossible. This paper proposes a parallel control strategy of variable parameter displacement velocity of permanent magnet synchronous linear motor based on composite neural network reconstruction object, which realizes the high-performance control of linear servo motor. Firstly, a parallel controller with variable parameters is designed using the error information of the moving displacement and linear velocity. Then, the displacement output of the permanent magnet synchronous linear motor is reconstructed by a composite radial basis neural network containing multi-dimensional information of the control object, and the partial derivative of the displacement to the control signal is obtained. Finally, based on the closed-loop stability condition of the system, a complete parameter update strategy of parallel controller with displacement velocity is provided according to the comparison between periodic retrieval errors and control targets. In the experiment, a permanent magnet synchronous linear servo system is designed, and different network observations, position controls, and object parameter controls are compared. The network comparison shows that the composite radial-based neural network has fast observation speed and high observation accuracy, and the observation time and root mean square error are 18.63 μs and 0.063 mm, respectively. The position control comparison shows that the variable parameter parallel algorithm has higher control precision than the fixed parameter algorithm, such as PID control, with a 30%~50% improvement effect in the displacement and velocity control error indexes. The parameter control comparison shows that in the variable parameter parallel control strategy, when the moving mass changes, the root mean square of displacement error is less than 0.015 mm, and the absolute maximum error is less than 0.060 mm, which means that the algorithm has stability and universality. The following conclusions can be drawn: (1) The composite RBF neural network structure has better observation performance than the traditional RBF neural network. Compared with the traditional improvement method, it has a better effect with less computing pressure on the processor. (2) Compared with the fixed coefficient algorithm, such as PID, the proposed variable parameter parallel control strategy can effectively improve the control performance of the actuator displacement. (3) The variable parameter parallel control strategy can guarantee the high precision control effect under different displacement settings and object parameters.
鲍明堃, 周扬忠. 基于复合神经网络重构对象的永磁同步直线电机变参数型位移速度并行控制[J]. 电工技术学报, 2024, 39(8): 2470-2484.
Bao Mingkun, Zhou Yangzhong. Parallel Displacement Velocity Control of Permanent Magnet Synchronous Linear Motor with Variable Parameters Based on Composite Neural Network Reconstruction Object. Transactions of China Electrotechnical Society, 2024, 39(8): 2470-2484.
[1] 武志涛, 李帅, 程万胜. 基于扩展滑模扰动观测器的永磁直线同步电机定结构滑模位置跟踪控制[J]. 电工技术学报, 2022, 37(10): 2503-2512. Wu Zhitao, Li Shuai, Cheng Wansheng.Fixed- structure sliding mode position tracking control for permanent magnet linear synchronous motor based on extended sliding mode disturbance observer[J]. Transactions of China Electrotechnical Society, 2022, 37(10): 2503-2512. [2] Birbilen U, Lazoglu I.Design and analysis of a novel miniature tubular linear actuator[J]. IEEE Transa- ctions on Magnetics, 2018, 54(4): 1-6. [3] Wang Ze, Hu Chuxiong, Zhu Yu, et al.Newton-ILC contouring error estimation and coordinated motion control for precision multiaxis systems with com- parative experiments[J]. IEEE Transactions on Industrial Electronics, 2018, 65(2): 1470-1480. [4] Liu Yachao, Gao Jian, Zhong Yongbin, et al.Extended state observer-based IMC-PID tracking control of PMLSM servo systems[J]. IEEE Access, 2021, 9: 49036-49046. [5] 付培华, 陈振, 丛炳龙, 等. 基于反步自适应滑模控制的永磁同步电机位置伺服系统[J]. 电工技术学报, 2013, 28(9): 288-293, 301. Fu Peihua, Chen Zhen, Cong Binglong, et al.A position servo system of permanent magnet syn- chronous motor based on back-stepping adaptive sliding mode control[J]. Transactions of China Electrotechnical Society, 2013, 28(9): 288-293, 301. [6] 邵佳威, 蒋全, 倪燕青, 等. 基于改进自抗扰控制的永磁同步电机位置控制策略[J]. 控制工程, 2022, 29(8): 1487-1496. Shao Jiawei, Jiang Quan, Ni Yanqing, et al.Position control strategy of permanent magnet synchronous motor based on improved active disturbance rejection control[J]. Control Engineering of China, 2022, 29(8): 1487-1496. [7] 董顶峰, 黄文新, 卜飞飞, 等. 圆筒型反向式横向磁通直线电机定位力补偿二阶自抗扰控制器位置控制[J]. 电工技术学报, 2021, 36(11): 2365-2373. Dong Dingfeng, Huang Wenxin, Bu Feifei, et al.Second-order ADRC position control with detent force compensation for tubular reversal transverse flux linear machine[J]. Transactions of China Electrotechnical Society, 2021, 36(11): 2365-2373. [8] 许叙遥, 林辉. 基于动态滑模控制的永磁同步电机位置速度一体化设计[J]. 电工技术学报, 2014, 29(5): 77-83. Xu Xuyao, Lin Hui.Integrated design for permanent magnet synchronous motor servo systems based on dynamic sliding mode control[J]. Transactions of China Electrotechnical Society, 2014, 29(5): 77-83. [9] Zhang Jianhua, Zhou Shuqing, Ren Mifeng, et al.Adaptive neural network cascade control system with entropy-based design[J]. IET Control Theory & Applications, 2016, 10(10): 1151-1160. [10] 孙强, 张为堂. 磁通切换永磁电机模糊自适应PI控制策略[J]. 中国电机工程学报, 2017, 37(22): 6611-6618, 6773. Sun Qiang, Zhang Weitang.An adaptive-fuzzy PI control strategy for flux-switching permanent magnet motors[J]. Proceedings of the CSEE, 2017, 37(22): 6611-6618, 6773. [11] 黄依婷, 沈建新, 王云冲, 等. 基于递推最小二乘法观测器的永磁同步伺服电机变参数滑模控制[J]. 中国电机工程学报, 2022, 42(18): 6835-6846. Huang Yiting, Shen Jianxin, Wang Yunchong, et al.Variable parameter sliding mode control of permanent magnet synchronous servo motor based on recursive least square observer[J]. Proceedings of the CSEE, 2022, 42(18): 6835-6846. [12] 许德智, 黄泊珉, 杨玮林. 神经网络自适应的永磁直线同步电机超扭曲终端滑模控制[J]. 电力系统保护与控制, 2021, 49(13): 64-71. Xu Dezhi, Huang Bomin, Yang Weilin.Neural network adaptive super twist terminal sliding mode control for a permanent magnet linear synchronous motor[J]. Power System Protection and Control, 2021, 49(13): 64-71. [13] Wang Ze, Hu Chuxiong, Zhu Yu, et al.Neural network learning adaptive robust control of an industrial linear motor-driven stage with disturbance rejection ability[J]. IEEE Transactions on Industrial Informatics, 2017, 13(5): 2172-2183. [14] Yang Hongjun, Liu Jinkun.An adaptive RBF neural network control method for a class of nonlinear systems[J]. IEEE/CAA Journal of Automatica Sinica, 2018, 5(2): 457-462. [15] 唐志勇, 马福源, 裴忠才. 四旋翼的改进PSO-RBF神经网络自适应滑模控制[J/OL]. 北京航空航天大学学报: 1-14[2022-11-10]. DOI: 10.13700/j.bh. 1001-5965.2021.0477. Tang Zhiyong, Ma Fuyuan, Pei Zhongcai.Improved PSO-RBF neural network adaptive sliding mode control for quadrotor system[J/OL]. Journal of Beijing University of Aeronautics and Astronautics: 1-14[2022-11-10]. DOI: 10.13700/j.bh.1001-5965.2021. 0477. [16] 王爽心, 杨辉, 张秀霞. 基于混沌遗传算法的主汽温系统RBF-PID控制[J]. 中国电机工程学报, 2008, 28(23): 87-92. Wang Shuangxin, Yang Hui, Zhang Xiuxia.A novel RBF-PID control strategy for fresh steam temperature based on chaotic genetic algorithm[J]. Proceedings of the CSEE, 2008, 28(23): 87-92. [17] 李明, 封航, 张延顺. 基于UMAC的RBF神经网络PID控制[J]. 北京航空航天大学学报, 2018, 44(10): 2063-2070. Li Ming, Feng Hang, Zhang Yanshun.RBF neural network tuning PID control based on UMAC[J]. Journal of Beijing University of Aeronautics and Astronautics, 2018, 44(10): 2063-2070. [18] 何海军, 蒙西, 汤健, 等. 基于ET-RBF-PID的城市固废焚烧过程炉膛温度控制方法[J]. 控制理论与应用, 2022, 39(12): 2262-2273. He Haijun, Meng Xi, Tang Jian, et al.ET-RBF-PID- based control method for furnace temperature of municipal waste incineration process[J]. Control Theory & Applications, 2022, 39(12): 2262-2273. [19] 王卓, 王玉静, 王庆岩, 等. 基于协同深度学习的二阶段绝缘子故障检测方法[J]. 电工技术学报, 2021, 36(17): 3594-3604. Wang Zhuo, Wang Yujing, Wang Qingyan, et al.Two stage insulator fault detection method based on collaborative deep learning[J]. Transactions of China Electrotechnical Society, 2021, 36(17): 3594-3604. [20] 杨旭红, 陈阳, 贾巍, 等. 基于RBF神经网络的电压外环滑模控制的Vienna整流器[J]. 电力系统保护与控制, 2022, 50(18): 103-115. Yang Xuhong, Chen Yang, Jia Wei, et al.Vienna rectifier with voltage outer loop sliding mode control based on RBF neural network[J]. Power System Protection and Control, 2022, 50(18): 103-115. [21] 杜晓闯, 涂红兵, 黎岢, 等. 基于径向基神经网络仿真γ能谱模板库的核素识别方法[J]. 清华大学学报(自然科学版), 2021, 61(11): 1308-1315. Du Xiaochuang, Tu Hongbin, Li Ke, et al.Radionu- clide identification method based on a gamma-spectra template library simulated by radial basis function neural networks[J]. Journal of Tsinghua University (Science and Technology), 2021, 61(11): 1308-1315.