A learning algorithm for improved hybrid force control of robot arms

An investigation on the hybrid force control of robot arms by learning is presented. A well-known force control scheme based on feedback linearization is used to build up an algorithm which improves, trial by trial, force and position tracking over a finite time interval. Differently from other published learning control schemes, the proposed algorithm does not rely on high gain feedback. Robustness and convergence in spite of sufficiently small system parameter uncertainties and disturbances is proven by means of the contraction mapping principle.