Data-Based H ∞ ${H_\infty }$ Optimal Tracking Control of Completely Unknown Linear Systems Under Input Constraints

Abstract

This paper presents an $H_{\infty }$ optimal tracking control approach for linear systems with unknown models and input constraints. The proposed method is based on data-based adaptive dynamic programming (ADP) that is computationally tractable and does not require model approximation. This study consists of two new algorithms: a model-based constrained control algorithm and a data-based algorithm for systems with completely unknown models. A lower bound for the $H_{\infty }$ attenuation coefficient is determined to ensure optimality. Additionally, the approach allows for constraints on the amplitude and frequency of the control signal, which are incorporated using the idea of inverse optimal control (IOC). The effectiveness of the proposed method is demonstrated through a simulation example, showcasing its ability to achieve robust tracking performance and satisfy input constraints.