基于改进粒子群优化算法的永磁球形电机驱动策略研究  

Improved Particle Swarm Optimization Algorithm Based Driving Strategy Research for Permanent Magnet Spherical Motor

作　　者：周嗣理 李国丽[2,3] 王群京[2,4] 郑常宝[2,3] 文彦[2,5] Zhou Sili;Li Guoli;Wang Qunjing;Zheng Changbao;Wen Yan(School of Computer Science and Technology Anhui University,Hefei 230601 China;National Engineering Laboratory of Energy-Saving Motor&Control Technology Anhui University,Hefei 230601 China;School of Electrical Engineering and Automation Anhui University,Hefei 230601 China;Anhui Collaborative Innovation Center of Industrial Energy-Saving and Power Quality Control Anhui University,Hefei 230601 China;School of Internet Anhui University,Hefei 230601 China)

机构地区：[1]安徽大学计算机科学与技术学院,合肥230601 [2]安徽大学高节能电机及控制技术国家地方联合实验室,合肥230601 [3]安徽大学电气与自动化工程学院,合肥230601 [4]安徽大学工业节电与电能质量控制安徽省级协同创新中心,合肥230601 [5]安徽大学互联网学院,合肥230601

出　　处：《电工技术学报》2023年第1期166-176,189,共12页Transactions of China Electrotechnical Society

基　　金：国家自然科学基金(51637001);安徽省自然基金(2008085ME156)资助项目。

摘　　要：永磁球形电机(PMSpM)是一种结构紧凑、可多自由运动的单关节传动装置。该文提出一种适用于PMSpM驱动策略优化的改进粒子群优化(IPSO)算法,该算法可实时计算PMSpM期望转矩所对应的线圈驱动电流。首先,通过圆环函数建立PMSpM转矩解析模型,并构建转矩Map图;然后,在确定种群数量后为标准粒子群优化(PSO)算法引入自适应动态惯性权重和自适应学习因子,将所提IPSO算法与PSO算法进行仿真对比,仿真结果表明,在同样的精度下采用IPSO算法计算驱动电流比采用PSO算法有更快的计算速度;最后,通过PMSpM控制试验进一步证明了该仿真结论的正确性。A permanent magnet spherical motor(PMSpM) is a compact transmission apparatus that is capable of motion in multiple degrees of freedom. To achieve the close loop control of the PMSpM, the driving current of the stator coils needs to be calculated, and the analytic torque model needs to be built in advance.However, if the geometry of the permanent magnet(PM) is a non-circumferential symmetric one, the pseudoinverse matrix technique is not applicable. Thus, the research on the fast driving strategy of the universal reverse torque model is an essential prerequisite for the PMSpM close-loop control.This paper takes the PMSpM with the stepped cylindrical PM as the research object. Firstly, this paper proposes new analytical torque models using the toroidal expansion method. To avoid repeating integrations in magnetic and torque analytic calculation, this paper builds torque maps by moving one 1A energized electromagnetic coil on the overall spherical surface of the airgap along the azimuth angle direction and polar angle direction. Secondly, the classical particle swarm optimization algorithm(PSO) is introduced to build the reverse torque model. The current of the stator electromagnetic coils is considered as the particle swarm, and the desired torques are set as optimization targets. Thus, we can use the reverse torque model to calculate the driving current of the stator electromagnetic coils from the torque maps. Thirdly, this paper proposes an improved particle swarm optimization(IPSO) algorithm for the PMSpM driving strategy optimization, which can be used for calculating the real-time driving current for the desired torques of the PMSpM. After the determination of the population size of the PSO algorithm, the adaptive dynamic inertia weight and adaptive learning factors are introduced for IPSO.Simulation results on the IPSO algorithm optimization show that the improvement of the classical PSO algorithm is significantly effective. A typical population size can generate convergence before 250 iterations. The larger

关 键 词：永磁球形电机 改进粒子群优化 自适应动态惯性权重 自适应学习因子 驱动电流 

分 类 号：TM351[电气工程—电机] TP18[自动化与计算机技术—控制理论与控制工程]