A Novel Tuning Method for PD Control of Robotic Manipulators Based on Minimum Jerk Principle

In this paper we introduce a novel technique for optimal tuning of PD controllers engaged in tracking minimum-jerk (MJ) trajectories. The proposed approach is an attempt to bridge the gap between the MJ principle for trajectory planning, which is based solely on the robot's kinematics, and the optimal estimation of the gains of the joint controllers, which depends on the robot dynamics. For this purpose we define an objective function that combines kinematic and dynamic-based performance indices and which is minimized via a genetic algorithm that searches for optimal gains for the joint controllers. The proposed approach is shown to perform consistently better than the standard PD control for tracking MJ trajectories.