Data-Driven Controller Parameter Tuning for Nonlinear Systems using Backstepping Method

Abstract

This study considers data-driven controller parameter tuning for nonlinear systems using one-shot input-output data. The study addresses the model reference problem for a nonlinear system described by the parametric strict-feedback form and determines the controller parameters from one-shot experimental data. In the proposed method, the idea of the VRFT (Virtual Reference Feedback Tuning) is applied to the tuning of nonlinear controller parameters. Finally, this paper shows numerical simulation results to demonstrate the effectiveness of the proposed method.