Optimal Control Applied to Wheeled Mobile Vehicles

The goal of the work described in this paper is to develop a particular optimal control technique based on a Cell-Mapping technique in combination with the Q-learning reinforcement learning method to control wheeled mobile vehicles. This approach manages 4 state variables due to a dynamic model is performed instead of a kinematics model which can be done with less variables. This new solution can be applied to non-linear continuous systems where reinforcement learning methods have multiple constraints. Emphasis is given to the new combination of techniques, which applied to optimal control problems produce satisfactory results. The proposed algorithm is very robust to any change involved in the vehicle parameters because the vehicle model is estimated in real time from received experience.