Iterative Learning Control with Variable Trajectory Length in the Presence of Noise

This scientific paper addresses the problem of iterative learning control for the design of linear discrete-time multiple-input-multiple-output (MIMO) systems, where the system contains nonrepetitive (iteration-dependent) load noise and measurement disturbances noise. Under the condition that the trajectory length varies with iteration, the effects of disturbances noise and loss of system output error information are dealt with by mathematical expectation, and the tracking error convergence is achieved by using open-closed-loop law. Among them, the feedforward part ensures the convergence of the ILC tracking error in the sense of mathematical expectation. The feedback control part is used to compensate for the loss of tracking information in previous iterations using the tracking information of the current iteration. Through rigorous mathematical induction analysis, the convergence conditions are derived. Finally, the effectiveness of the control law is proved by the actual simulation experiments for this kind of system.