  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.
