AN ITERATIVE SCHEME FOR LEARNING GRAVITY COMPENSATION IN FLEXIBLE ROBOT ARMS

Mimicking the case of rigid robot arms, the set-point regulation probl em for ManipUlators with flexible links moving under gravity can be so lved by either model-bawd compensation or PID control. The former cann ot be applied if an unknown payload is present or when model parameter s are poorly estimated, while the latter requires fine and lengthy tun ing of gains in order to achieve good performance on the whole workspa ce. Moreover, no global convergence proof has been yet given for PID c ontrol of flexible robot arms. In this paper, a simple iterative schem e is proposed for generating exact gravity compensation at the desired set-point, without the knowledge of rigid or flexible dynamic model t erms. The control law starts with a PD action on the error at the join t level, updating at discrete instants an additional feedforward term. Global convergence of the scheme is proved under a mild condition on the proportional gain and a structural property on the arm stiffness, which is usually satisfied in practice. The proposed learning scheme i s also extended to the direct control of the end-effector (tip) positi on. Experimental results are presented for a two-link robot with a fle xible forearm moving on a tilted plane.