Adaptive Iterative Learning Control for Non-equal Length Tasks in the Presence of Initial Errors

Abstract

Both non-equal trail lengths and initial condition problems are practical challenges to learning control of robotic and mechatronic systems. Iterative learning to update input is still desired, because of the repetitive motion nature of the controlled objects. This paper concerns with the adaptive iterative learning control method for performing the non-identical tracking tasks, where the time scaling technique is used to normalize the non-equal trial lengths, while the error-tracking approach is adopted for coping with initial errors. Theoretical results for the performance analysis are presented in detail. The uniform convergence of the tracking error is examined, while the boundedness of the variables in the closed-loop is characterized. The proposed control design method does not require the magnitude transformation, and removes the assumption of identical initial conditions. The time scaling technique is effective for assuring the expected tracking performance for the non-equal-length tasks in the presence of initial errors.