An Iterative Learning Control of Robot Manipulators

An iterative learning scheme comprising a unique feedforward learning controller and a linear feedback controller is presented. In the feedback loop, the fixed-gain PD controller provides a stable open neighborhood along a desired trajectory. In the feedforward path, on the other hand, a learning control strategy is exploited to predict the desired actuator torques. It is shown that the predicted actuator torque converges to the desired one as the iteration number increases. The convergence is established based on the Lyapunov stability theory. The proposed learning scheme is structurally simple and computationaly efficient. Moreover, it posesses two major advantages: the ability to reject unknown deterministic disturbances and the ability to adapt itself to the unknown system parameters.