An Optimal Iterative Learning Control Approach for Linear Systems With Nonuniform Trial Lengths Under Input Constraints

In practical applications of iterative learning control (ILC), the repetitive process may end up early by accident during the performance improvement along the trial axis, which yields the nonuniform trial length problem. For such practical systems, input signals are usually constrained because of some certain physical limitations. This article proposes an optimal ILC algorithm for linear time-invariant multiple-input-multiple-output (MIMO) systems with nonuniform trial lengths under input constraints. The optimal ILC framework is specifically modified for the nonuniform trial length problem, where the primal-dual interior point method is introduced to deal with the input constraints. Hence, the constraint handling capability are improved compared with the conventional counterparts for nonuniform trial lengths. Also, the monotonic convergence property of the proposed optimal ILC algorithm is obtained in the sense of mathematical expectation. Finally, the effectiveness of the proposed algorithm is verified on the numerical simulation of a mobile robot.

Automated summary

This article proposes an optimal iterative learning control (ILC) algorithm for linear time-invariant multiple-input-multiple-output (MIMO) systems with nonuniform trial lengths under input constraints. The algorithm introduces the primal-dual interior point method to handle the input constraints, improving the constraint handling capability compared to conventional methods for nonuniform trial lengths. The algorithm also exhibits monotonic convergence property and its effectiveness is verified through numerical simulation of a mobile robot.