Iterative Learning Control for Unknown Nonlinear Systems Based on Data-Driven Model-Free Feedback Linearization
Abstract
This paper proposes a novel iterative learning control (ILC) scheme for unknown affine nonlinear systems by integrating model-free feedback linearization with two-dimensional (2D) structure of the controlled dynamics. The approach eliminates the requirement for prior model knowledge by employing model reference adaptive control (MRAC) and Q-learning to achieve feedback linearization of unknown nonlinear systems. A computationally efficient method is developed to estimate feedback linearization parameters using historical data from previous trials. Upon obtaining the linearized system representation, the control design is performed within the 2D system setting, resulting in a set of linear matrix inequality (LMI) constraints that leads to the ILC law. The efficacy of the proposed approach is validated through numerical experiments on an inverted pendulum system, demonstrating high-precision trajectory tracking across iterative executions.