Hybrid force/position control for robot manipulators based on a D-type learning law

Abstract

Summary A learning control algorithm is proposed for hybrid force/position tracking of a manipulator in contact with a hard surface. The algorithm utilizes acceleration errors and force errors for learning the inputs required to perform repetitive tasks. We prove the convergence of both force and position tracking errors theoretically. The speed of convergence of the tracking errors is shown to be improved by proper choice of learning gains. The method is compared with PI and PID learning, and similarities with the free-space case are outlined. The robustness of the new scheme to uncertainties in surface geometry and link parameters is discussed. Results of simulation studies and experimental implementation on 3-link robot manipulators are presented to illustrate the effectiveness of the proposed learning controller.