A Norm Optimal Iterative Learning Control Approach With Efficient Computation

Iterative learning control (ILC) is excellently applied to output tracking for systems that perform repetitive tasks. Classical norm optimal ILC (NOILC) approach yields optimal control inputs by minimizing a quadratic cost function. However, this approach requires the introduction of supervectors, i.e., lifting technique, for system description, which raises a high computational load problem. The contribution of this paper is to propose a non-lifted NOILC (N-NOILC) approach by redesigning a cost function that does not involve the lifting technique, achieving the goal of efficient computation. Therefore, the proposed approach is particularly suitable for large tracking tasks with long trial duration and high sampling frequency. Moreover, considering the model uncertainty in practice, robust convergence is analyzed. Numerical simulations are shown to demonstrate the properties and advantages of the proposed approach.