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

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.
Funding Agencies|Brazilian National Council for Scientific and Technological Development-CNPqConselho Nacional de Desenvolvimento Cientifico e Tecnologico (CNPQ) [465755/2014-3, 304201/2018-9]; Coordination of Improve-ment of Higher Education Personnel-Brazil-CAPESCoordenacao de Aperfeicoamento de Pessoal de Nivel Superior (CAPES) [001, 88887.136349/2017-00]; SAo Paulo Research Foundation-FAPESP, BrazilFundacao de Amparo a Pesquisa do Estado de Sao Paulo (FAPESP) [2014/50851-0]; Minas Gerais Research Foundation-FAPEMIG, Brazil [PPM 00337/17]