| Full text | |
| Author(s): |
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]
Total Authors: 6
|
| Affiliation: | [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
Total Affiliations: 3
|
| Document type: | Journal article |
| Source: | EXPERT SYSTEMS WITH APPLICATIONS; v. 192, APR 15 2022. |
| Web of Science Citations: | 0 |
| 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. (AU) | |
| FAPESP's process: | 14/50851-0 - INCT 2014: National Institute of Science and Technology for Cooperative Autonomous Systems Applied in Security and Environment |
| Grantee: | Marco Henrique Terra |
| Support Opportunities: | Research Projects - Thematic Grants |