ON THE ROLE OF HIGH-GAIN FEEDBACK IN P-TYPE LEARNING CONTROL OF ROBOTARMS

An alternative proof of the convergence of known P-type learning contr ol schemes for unconstrained and constrained robot arms is presented. The analysis carried out is based on a singular perturbation approach and points out the role played by high gain velocity and force feedbac ks and by actuator/output co-location. The singular perturbation analy sis developed clearly displays that the P-type learning algorithm is g eometrically convergent, and that, thanks to the high gain feedback, t his convergence does not depend on the knowledge of the robot paramete rs. Robustness with respect to some classes of disturbances is also ad dressed. The stability of the high gain closed loop system in case of robots in contact with the environment is shown to rely on a sufficien tly good knowledge of the constraining surface.