Optimal Output Feedback Controller Design of Permanent Magnet Synchronous Motor Speed Servo System
-
摘要: 针对永磁同步电动机(Permanent magnet synchronous motor, PMSM)模型参数未知以及电枢电流和负载转矩无法直接测量的问题, 设计一种基于自适应动态规划(Adaptive dynamic programming, ADP)的输出反馈控制方案, 实现PMSM最优速度跟踪控制. 首先, 根据PMSM内部特性确定其数学模型的结构, 构建与原始系统相对应的辅助系统, 引入新的线性二次指标来实现速度最优跟踪调节. 其次, 设计一种嵌入式观测器, 该观测器能够在系统模型未知情况下用可测量数据重构系统全部状态. 此外, 提出一种离线策略的ADP方法逼近最优控制增益的解. 最后, 仿真结果验证所提控制方案在模型参数未知以及电枢电流和负载转矩不可测量的情况下, 实现了精确的速度跟踪性能和良好的瞬态响应, 同时降低了电压的冲击.Abstract: In response to the problems of unknown model parameters of permanent magnet synchronous motor (PMSM) and inability to directly measure armature current and load torque, this paper designs an output feedback control scheme based on adaptive dynamic programming (ADP) to achieve optimal speed tracking control for PMSM. Firstly, based on the internal characteristics of the PMSM, the mathematical model＇s structure is determined. An auxiliary system corresponding to the original system is constructed, and a new linear quadratic index is introduced to achieve optimal speed tracking adjustment. Next, an embedded observer is designed that can reconstruct all system states with measurable data in the presence of unknown system models. In addition, this paper proposes an off-policy ADP method to approximate the solution of the optimal control gain. Finally, the simulation results verify that the proposed control scheme achieves precise speed tracking performance and good transient response in the presence of unknown model parameters and unmeasurable armature current and load torque, while reducing voltage shocks.
-
图 4 不同控制方法下转子速度的跟踪效果
Fig. 4 Tracking effect of rotor speed under different control methods
图 8 方法2和方法4下的$ q $轴电压响应局部放大
Fig. 8 Partial amplification of q-axis voltage response under method 2 and method 4
图 9 不同权重$ Q $下的转子速度跟踪效果
Fig. 9 Tracking effect of rotor speed under different weights $ Q $
图 10 不同权重$ Q $下的$ q $轴电压响应
Fig. 10 q-axis voltage response under different weights $ Q $
图 11 不同权重$ R $下的转子速度跟踪效果
Fig. 11 Tracking effect of rotor speed under different weights $ R $
图 12 不同权重$ R $下的$ q $轴电压响应
Fig. 12 q-axis voltage response under different weights $ R $
表 1 PMSM系统参数设置
Table 1 PMSM system parameters setting
参数 大小 单位 转动惯量$J$ $2.10\times 10^{-3}$ ${{\rm{kg}}{\cdot}{\rm{m}}}^2$ 粘性摩擦系数$B_s$ $5.71\times 10^{-3}$ ${{\rm{N}}{\cdot}{\rm{s/rad}}}$ 极对数$n_p$ $4$ — 永磁通链$\varphi$ $8.10\times 10^{-2}$ Wb 定子电感$L_s$ $9.80\times 10^{-3}$ H 定子电阻$R_s$ $1.06$ $\Omega$ 
下载: 导出CSV
-
[1] Yang J, Chen W H, Li S H, Guo L, Yan Y D. Disturbance/uncertainty estimation and attenuation techniques in PMSM drives——A survey. IEEE Transactions on Industrial Electronics, 2017, 64(4): 3273−3285 doi: 10.1109/TIE.2016.2583412 [2] Deniz E. ANN-based MPPT algorithm for solar PMSM drive system fed by direct-connected PV array. Neural Computing and Applications, 2017, 28(10): 3061−3072 doi: 10.1007/s00521-016-2326-4 [3] Li P, Xu X S, Yang S R, Jiang X F. Open circuit fault diagnosis strategy of PMSM drive system based on grey prediction theory for industrial robot. Energy Reports, 2023, 9: 313−320 doi: 10.1016/j.egyr.2022.10.433 [4] Wang M L, Ren X M, Chen Q. Cascade optimal control for tracking and synchronization of a multimotor driving system. IEEE Transactions on Control Systems Technology, 2019, 27(3): 1376−1384 doi: 10.1109/TCST.2018.2810273 [5] Errouissi R, AL-Durra A, Muyeen S M. Experimental validation of a novel PI speed controller for AC motor drives with improved transient performances. IEEE Transactions on Control Systems Technology, 2018, 26(4): 1414−1421 doi: 10.1109/TCST.2017.2707404 [6] Ruderman M, Iwasaki M, Chen W H. Motion-control techniques of today and tomorrow: A review and discussion of the challenges of controlled motion. IEEE Industrial Electronics Magazine, 2020, 14(1): 41−55 doi: 10.1109/MIE.2019.2956683 [7] Kim S K. Robust adaptive speed regulator with self-tuning law for surfaced-mounted permanent magnet synchronous motor. Control Engineering Practice, 2017, 61: 55−71 doi: 10.1016/j.conengprac.2017.01.014 [8] Wu J, Zhang J D, Nie B C, Liu Y H, He X K. Adaptive control of PMSM servo system for steering-by-wire system with disturbances observation. IEEE Transactions on Transportation Electrification, 2022, 8(2): 2015−2028 doi: 10.1109/TTE.2021.3128429 [9] 谢浩然, 胡纯福, 卢萌, 刘晓, 黄守道. 基于级联线性−非线性自抗扰控制器的永磁直线同步电机速度控制策略研究. 中国电机工程学报, 2024, 44(15): 6158−6168Xie Hao-Ran, Hu Chun-Fu, Lu Meng, Liu Xiao, Huang Shou-Dao. Research on speed control strategy for permanent magnet linear synchronous motor based on cascaded linear-nonlinear active disturbance rejection controller. Proceedings of the CSEE, 2024, 44(15): 6158−6168 [10] 徐睿琦, 张昆鹏, 林欣魄, 孔德山, 刘壮, 刘健行. 基于高阶滑模观测器的永磁同步电机无差拍预测电流控制. 控制理论与应用, 2023, 40(11): 1990−1998 doi: 10.7641/CTA.2023.20476Xu Rui-Qi, Zhang Kun-Peng, Lin Xin-Po, Kong De-Shan, Liu Zhuang, Liu Jian-Xing. Deadbeat predictive current control of permanent magnet synchronous motor based on higher order sliding mode observer. Control Theory & Applications, 2023, 40(11): 1990−1998 doi: 10.7641/CTA.2023.20476 [11] 卢宏平, 赵文祥, 陶涛, 王化南, 钱渊方, 王政. 永磁同步电机低载波比精确无差拍预测电流控制. 中国电机工程学报, DOI: 10.13334/j.0258-8013.pcsee.231871Lu Hong-Ping, Zhao Wen-Xiang, Tao Tao, Wang Hua-Nan, Qian Yuan-Fang, Wang Zheng. Precise deadbeat predictive current control of PMSM with low carrier ratio. Proceedings of the CSEE, DOI: 10.13334/j.0258-8013.pcsee.231871 [12] 张化光, 张欣, 罗艳红, 杨珺. 自适应动态规划综述. 自动化学报, 2013, 39(4): 303−311 doi: 10.1016/S1874-1029(13)60031-2Zhang Hua-Guang, Zhang Xin, Luo Yan-Hong, Yang Jun. An overview of research on adaptive dynamic programming. Acta Automatica Sinica, 2013, 39(4): 303−311 doi: 10.1016/S1874-1029(13)60031-2 [13] Gao W N, Jiang Z P. Adaptive dynamic programming and adaptive optimal output regulation of linear systems. IEEE Transactions on Automatic Control, 2016, 61(12): 4164−4169 doi: 10.1109/TAC.2016.2548662 [14] Wei Q L, Liu D R, Lin H Q. Value iteration adaptive dynamic programming for optimal control of discrete-time nonlinear systems. IEEE Transactions on Cybernetics, 2016, 46(3): 840−853 doi: 10.1109/TCYB.2015.2492242 [15] Lu J W, Wei Q L, Wang F Y. Parallel control for optimal tracking via adaptive dynamic programming. IEEE/CAA Journal of Automatica Sinica, 2020, 7(6): 1662−1674 doi: 10.1109/JAS.2020.1003426 [16] 王睿, 孙秋野, 张化光. 微电网的电流均衡/电压恢复自适应动态规划策略研究. 自动化学报, 2022, 48(2): 479−491Wang Rui, Sun Qiu-Ye, Zhang Hua-Guang. Research on current sharing/voltage recovery based adaptive dynamic programming control strategy of microgrids. Acta Automatica Sinica, 2022, 48(2): 479−491 [17] 罗彪, 欧阳志华, 易昕宁, 刘德荣. 基于自适应动态规划的移动机器人视觉伺服跟踪控制. 自动化学报, 2023, 49(11): 2286−2296Luo Biao, Ouyang Zhi-Hua, Yi Xin-Ning, Liu De-Rong. Adaptive dynamic programming based visual servoing tracking control for mobile robots. Acta Automatica Sinica, 2023, 49(11): 2286−2296 [18] Wang Z Y, Wang Y Q, Davari M, Blaabjerg F. An effective PQ-decoupling control scheme using adaptive dynamic programming approach to reducing oscillations of virtual synchronous generators for grid connection with different impedance types. IEEE Transactions on Industrial Electronics, 2024, 71(4): 3763−3775 doi: 10.1109/TIE.2023.3279564 [19] Wang Z Y, Yu Y J, Gao W N, Davari M, Deng C. Adaptive, optimal, virtual synchronous generator control of three-phase grid-connected inverters under different grid conditions——An adaptive dynamic programming approach. IEEE Transactions on Industrial Informatics, 2022, 18(11): 7388−7399 doi: 10.1109/TII.2021.3138893 [20] Qasem O, Davari M, Gao W N, Kirk D R, Chai T Y. Hybrid iteration ADP algorithm to solve cooperative, optimal output regulation problem for continuous-time, linear, multiagent systems: Theory and application in islanded modern microgrids with IBRs. IEEE Transactions on Industrial Electronics, 2024, 71(1): 834−845 doi: 10.1109/TIE.2023.3247734 [21] Ping Z W, Jia Y J, Xiong B G, Zhang H W, Lu J G. Optimal output regulation for PMSM speed servo system using approximate dynamic programming. Science China Information Sciences, 2023, 66(7): Article No. 170206 [22] Khiabani A G, Heydari A. Optimal torque control of permanent magnet synchronous motors using adaptive dynamic programming. IET Power Electronics, 2020, 13(12): 2442−2449 doi: 10.1049/iet-pel.2019.1339 [23] Tan L N, Pham T C. Optimal tracking control for PMSM with partially unknown dynamics, saturation voltages, torque, and voltage disturbances. IEEE Transactions on Industrial Electronics, 2022, 69(4): 3481−3491 doi: 10.1109/TIE.2021.3075892 [24] Zhao J G, Yang C Y, Gao W N, Zhou L N. Reinforcement learning and optimal control of PMSM speed servo system. IEEE Transactions on Industrial Electronics, 2023, 70(8): 8305−8313 doi: 10.1109/TIE.2022.3220886 [25] Rizvi S A A, Lin Z L. Reinforcement learning-based linear quadratn of continuous-time systems using dynamic output feedback. IEEE Transactions on Cybernetics, 2020, 50(11): 4670−4679 doi: 10.1109/TCYB.2018.2886735 [26] 庞文砚, 范家璐, 姜艺, Lewis Frank Leroy. 基于强化学习的部分线性离散时间系统的最优输出调节. 自动化学报, 2022, 48(9): 2242−2253Pang Wen-Yan, Fan Jia-Lu, Jiang Yi, Lewis Frank Leroy. Optimal output regulation of partially linear discrete-time systems using reinforcement learning. Acta Automatica Sinica, 2022, 48(9): 2242−2253 [27] Gao W N, Jiang Z P. Adaptive optimal output regulation of time-delay systems via measurement feedback. IEEE Transactions on Neural Networks and Learning Systems, 2019, 30(3): 938−945 doi: 10.1109/TNNLS.2018.2850520 [28] Wang Z Y, Wang Y Q, Kowalczuk Z. Adaptive optimal discrete-time output-feedback using an internal model principle and adaptive dynamic programming. IEEE/CAA Journal of Automatica Sinica, 2024, 11(1): 131−140 doi: 10.1109/JAS.2023.123759 [29] Gao W N, Liu Y Y, Odekunle A, Yu Y J, Lu P L. Adaptive dynamic programming and cooperative output regulation of discrete-time multi-agent systems. International Journal of Control, Automation and Systems, 2018, 16: 2273−2281 doi: 10.1007/s12555-017-0635-8 [30] Du H B, Wen G H, Cheng Y J, Lu J H. Design and implementation of bounded finite-time control algorithm for speed regulation of permanent magnet synchronous motor. IEEE Transactions on Industrial Electronics, 2021, 68(3): 2417−2426 doi: 10.1109/TIE.2020.2973904 [31] Krishnan R. Electric Motor Drives: Modeling, Analysis, and Control. Upper Saddle River: Prentice Hall, 2001. [32] Huang J. Nonlinear Output Regulation: Theory and Applications. Philadelphia: SIAM, 2004. [33] Wang Z Y, Yu Y J. Adaptive optimal control of CVCF inverters with uncertain load: An adaptive dynamic programming approach. IEEE Access, 2021, 9: 89276−89286 doi: 10.1109/ACCESS.2021.3090815 -
图(12) / 表(1)
计量
- 文章访问数: 316
- HTML全文浏览量: 113
- PDF下载量: 105
- 被引次数: 0
