Robust learning control for constrained robots

A learning control algorithm is developed for constrained motion control and force control for robot systems. The proposed controller is able to improve its performance in terms of both motion and force tracking errors in the presence of state disturbances, output measurement noises and errors in initial conditions as the operation is repeated. It is shown that the motion and force trajectories converge to neighborhoods of the desired trajectories and these neighborhoods tend to zero as the state disturbance, output measurement noise, and error in initial condition tend to zero