Self-Scheduling Robust Preview Controllers for Path Tracking and Autonomous Vehicles

TL;DR

This paper presents a method for designing gain scheduling robust preview controllers for autonomous vehicle path tracking using Linear Quadratic $H_ abla$ optimization within an LMI framework, emphasizing robustness and implementation simplicity.

Contribution

It introduces a systematic approach for synthesizing robust LPV controllers for autonomous vehicles using LMI-based $H_ abla$ optimization, considering practical implementation constraints.

Findings

Robust controllers effectively handle vehicle uncertainties.

Different LMI formulations are compared for performance.

Static output and state feedback controllers are feasible for implementation.

Abstract

In this study, we detail the procedures for designing gain scheduling controllers by Linear Quadratic $H_\infty$ robust optimization methods in Linear Matrix Inequalities (LMI) framework. The controllers are aimed at steering control of the autonomous vehicles. We first construct the Linear Parameter Varying (LPV) vehicle models and synthesize the robust controllers with uncertainty and nominal plants. We choose static output and state feedback controller structure to avoid higher order controllers considering implementation issues. The robust control problems are solved by using different LMI formulations and optimization weights with and without eigenvalue location constraints. The results are compared.