Robot Manipulator Anti-Disturbance Control based on PSO Multi-task Optimization

Abstract

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.