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

Autor(es)

de Morais, Gustavo A. Prudencio ^[1] ; Marcos, Lucas Barbosa ^[1] ; Barbosa, Filipe Marques ^[2] ; Barbosa, Bruno H. G. ^[3] ; Terra, Marco Henrique ^[1] ; Grassi, Jr., Valdir ^[1]

Afiliação do(s) autor(es)

^[1] Univ Sao Paulo, Sao Carlos Sch Engn, Dept Elect & Comp Engn, Sao Carlos - Brazil

^[2] Linkoping Univ, Dept Elect Engn, Div Automat Control, Linkoping - Sweden

^[3] Univ Fed Lavras, Dept Automat, Lavras - Brazil

• Tipo de documento: Artigo Científico
• Fonte: EXPERT SYSTEMS WITH APPLICATIONS; v. 192, APR 15 2022.
• Citações Web of Science: 0

Resumo

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. (AU)

• Processo FAPESP: 14/50851-0 - INCT 2014: Instituto Nacional de Ciência e Tecnologia para Sistemas Autônomos Cooperativos Aplicados em Segurança e Meio Ambiente
• Beneficiário: Marco Henrique Terra
• Modalidade de apoio: Auxílio à Pesquisa - Temático