A Kernel-based Learning Approach for Nonlinear MIMO Systems in an Iterative Learning Control Framework

This paper introduces a kernel-based method for learning feedforward controllers within an Iterative Learning Control (ILC) framework tailored for nonlinear processes. Unlike traditional ILC algorithm that relies on the knowledge of first principle-based models, this approach leverages a data-driven methodology to develop an iterative control update rule using kernel-based training. We compared this method against a traditional ILC scheme and a baseline neural network-based approach. The effectiveness of the proposed method is demonstrated through a unicycle path-following control problem, evaluated across various simulated test scenarios. Performance metrics include vehicle tracking error and ILC convergence speed, confirming the effectiveness of the proposed data-driven approach.