Design and implementation of adaptive and repetitive controllers for mechanical manipulators

Synthesis and implementation results for a recently developed class of adaptive and repetitive controllers used for motion control of mechanical manipulators are presented. The repetitive controller, which learns the input torque corresponding to a repetitive desired trajectory, requires no explicit knowledge of the manipulator equations of motion. The adaptive controller, on the other hand, which estimates the robot dynamic parameters online, may be used for more general trajectories but requires more detailed modeling information. Both schemes are computationally efficient and require no acceleration feedback of any kind; only standard position and velocity feedback information is utilized. The performances of the above-mentioned controllers were experimentally evaluated on an IBM 7545 robotic manipulator and the results were compared to those of a simple PD (proportional-derivative) feedback system and a compound-torque controller. The repetitive algorithm outperformed the other controllers by a significant margin in terms of the tracking accuracy