Robust path-following control design of heavy vehicles based on multiobjective evolutionary optimization

TL;DR

This paper introduces a robust recursive control method for heavy vehicles that uses multiobjective evolutionary optimization and local search to enhance robustness, stability, and smoothness in uncertain environments.

Contribution

It proposes a novel robust recursive controller designed via multiobjective optimization combined with local search, applicable to existing evolutionary algorithms.

Findings

Improved robustness and stability of heavy vehicle control systems.

Enhanced system smoothness and performance under uncertainties.

Effective integration of model-based control with machine learning techniques.

Abstract

The ability to deal with systems parametric uncertainties is an essential issue for heavy self-driving vehicles in unconfined environments. In this sense, robust controllers prove to be efficient for autonomous navigation. However, uncertainty matrices for this class of systems are usually defined by algebraic methods which demand prior knowledge of the system dynamics. In this case, the control system designer depends on the quality of the uncertain model to obtain an optimal control performance. This work proposes a robust recursive controller designed via multiobjective optimization to overcome these shortcomings. Furthermore, a local search approach for multiobjective optimization problems is presented. The proposed method applies to any multiobjective evolutionary algorithm already established in the literature. The results presented show that this combination of model-based controller and machine learning improves the effectiveness of the system in terms of robustness, stability and smoothness.