粒子群优化
控制理论(社会学)
趋同(经济学)
计算机科学
控制器(灌溉)
任务(项目管理)
模式(计算机接口)
选择(遗传算法)
过程(计算)
滑模控制
控制(管理)
控制工程
工程类
人工智能
算法
非线性系统
物理
操作系统
经济
系统工程
生物
量子力学
经济增长
农学
作者
Yifan Chen,Miaomiao Qu,Xiaoyan Shi
标识
DOI:10.1109/docs55193.2022.9967483
摘要
A new optimal anti-disturbance sliding mode control approach for manipulators is proposed in this paper. Aiming at the difficulty of parameter selection of sliding mode controller for manipulators, instead of empirical trial and error design approach, it is proposed a multi-task transfer strategy of surrogate-assisted Particle Swarm Optimization (PSO) approach, to solve the problem of optimal control parameter selection in the time-consuming adjustment process. The experimental results show that compared with the traditional PSO algorithm, the approach in this paper can effectively improve the convergence speed and control effect. The performance of the controller based on this optimization approach is superior to that based on the traditional PSO algorithm in terms of dynamic and static performance.
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