A frequency-domain approach to learning control: implementation for a robot manipulator

A frequency-domain approach to the analysis and design of a learning control law for linear dynamic systems is presented. In its most simple version the scheme uses two separate filters in order to achieve rapid improvements in a specified bandwidth while cutting off-perhaps unmodeled-dynamic effects which would bar the convergence. The merit of this approach is to make explicit the tradeoff between global convergence conditions and approximate learning of trajectories. The proposed learning controller can also be applied to robot manipulators for exact tracking of repetitive trajectories. The scheme has been implemented successfully for learning the actuator inputs which permit accurate reproduction of robot trajectories defined in the joint space. Experimental results are reported which also show the efficacy of this learning scheme in avoiding the possible occurrence of an unstable behavior