Adaptive repetitive learning control of servo mechanisms

In this paper, adaptive repetitive learning control is presented for trajectory tracking of servo mechanisms over the entire operation interval. Through the introduction of a novel Lyapunov-like function, the proposed adaptive learning control only requires the system to start from where it stopped at the last cycle, and avoids the strict requirement for initial repositioning for all the cycles. In addition, it is easily implementable as it only requires the joint position and velocity measurements which are easy to obtain, rather than the acceleration measurement as required by a number of traditional learning controllers. All the signals in the closed-loop are guaranteed to be bounded and the iterative trajectories are proven to follow the entire profile of the desired trajectory.