Stochastic Zeroth-Order Optimisation-Based Iterative Learning Control

Iterative learning control (ILC) improves the tracking performance of a system working in a repetitive mode by learning from previous trials. The existing ILC algorithms can achieve high performance but often with the use of a system model or careful parameter tuning. To address this limitation, we propose an alternative approach: stochastic zeroth-order (ZO) optimisation-based ILC. The proposed algorithm can achieve good convergence performance without using a system model or deliberate parameter tuning. A convergence analysis is provided, and the effectiveness of the proposed algorithm is verified by a simulation example.