Auto-Tuning of GPC weights based on Particle Swarm Optimization applied to a Manipulator End-Effector Trajectory Tracking

Manipulators are becoming more and more common to perform many tasks, whose accomplishment is often related to the applied control law. Regarding to this issue, control theory is helpful as the proper controller choice may turn the manipulator into a handy tool. Within this context this work presents an automatic tuning method for the Generalized Predictive Controller (GPC) in order to tracking the trajectory of a manipulator end-effector. The strategy employs Particle Swarm Optimization (PSO) to properly determine GPC cost function weights at each iteration that lead to zero error tracking. The proposed controller is compared to classical approaches for three different trajectories with results showing a better performance and tracking accuracy for the proposed approach.