Optimal Trajectory Tracking Control With a 5R Parallel Robot

Abstract

This work examines the control characteristics of a 5R parallel robotic manipulator subjected to two control studies. Firstly, fundamental aspects of dynamics are presented. Then a brief review of Particle Swarm Optimization (PSO) and feedforward Neural Networks (NN) is undertaken. Subsequently, to tackle the challenging problem of controller parameter tuning for parallel robots in trajectory tracking scenarios, a multi objective optimization problem is formulated for automatic tuning using PSO. This offline method is comparatively evaluated to the Nelder-Mead (NM) sequential simplex optimization scheme. Several results are attained illustrating the strengths and weaknesses of this method for parallel robot control. Then, an adaptive NN model reference control scheme using PSO is proposed. This scheme is proposed as one possible way to take advantage of the strong properties of the PSO online. The scheme is tested and several observations are outlined.