A learning algorithm for hybrid force control of robot arms

An investigation of the hybrid force control of robot arms by learning is presented. A force control scheme based on feedback linearization is used to build an algorithm that improves, trial by trial, force and position tracking over a finite time interval. Unlike 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 proved by means of the contraction mapping principle.<
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