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IET Cyber-Systems and Robotics
IET Cyber-Systems and Robotics
    
基于变参数粒子群优化的上肢康复机器人系统动态参数识别
Jin Lei Wang1, Yafeng Li1, Aimin An1,2,3
1College of Electrical and Information Engineering, Lanzhou University of Technology, Lanzhou 730050, People's Republic of China 2Key Laboratory of Gansu Advanced Control for Industrial Processes, Lanzhou 730050, People's Republic of China 3National Demonstration Center for Experimental Electrical and Control Engineering Education, Lanzhou University of Technology, Lanzhou 730050, People's Republic of China
Dynamic parameter identification of upper-limb rehabilitation robot system based on variable parameter particle swarm optimisation
Jin Lei Wang1, Yafeng Li1, Aimin An1,2,3
1College of Electrical and Information Engineering, Lanzhou University of Technology, Lanzhou 730050, People's Republic of China 2Key Laboratory of Gansu Advanced Control for Industrial Processes, Lanzhou 730050, People's Republic of China 3National Demonstration Center for Experimental Electrical and Control Engineering Education, Lanzhou University of Technology, Lanzhou 730050, People's Republic of China
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摘要： 针对上肢康复机器人动态建模中存在的参数不确定问题，本文提出了一种基于变参数粒子群优化（PSO）的动态参数识别方法。基于系统的动态模型，该算法将基础粒子群算法的惯性参数和学习规律由固定参数变为随迭代次数变化的函数。它解决了基础粒子群算法早期搜索空间小，后期收敛速度慢的问题，大大提高了其识别精度。最后，通过对仿真结果的对比分析，与最小二乘算法（LS）和未改进的粒子群算法相比，该算法的识别精度有了很大的提高。该算法在实际控制系统中的控制效果也明显优于最小二乘算法和未改进的粒子群优化算法。
Abstract: To solve the problem of uncertain parameters in dynamic modelling of upper-limb rehabilitation robots, a dynamic parameter identification method based on variable parameters particle swarm optimisation (PSO) is developed. Based on the dynamic model of the system, the algorithm changes the inertia parameter and learning law of the basic PSO algorithm from the fixed-parameter to the function that changes with the number of iterations. It solves the problems of small search space in the early stage and slow convergence speed in the later stage of the basic PSO algorithm, which greatly improves its identification accuracy. Finally, through the comparison and analysis of the simulation results, compared with those of the least square (LS) and unmodified PSO identification algorithms, a great improvement in the identification accuracy of the algorithm is achieved. The control effect in the actual control system is also much better than those of the LS and PSO algorithms.
收稿日期: 2020-04-27
通讯作者: Aimin An     E-mail: anaiminll@163.com
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引用本文:

Jin Lei Wang, Yafeng Li, Aimin An. Dynamic parameter identification of upper-limb rehabilitation robot system based on variable parameter particle swarm optimisation. IET Cyber-Systems and Robotics, 10.1049/iet-csr.2020.0023.

链接本文:

http://www.zjujournals.com/iet-csr/CN/10.1049/iet-csr.2020.0023    或    http://www.zjujournals.com/iet-csr/CN/Y2020/V2/I3/1

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