This paper proposes a reinforcement learning (RL)-based approach for dynamically selecting the most suitable control method according to changing operating conditions. Using the nominal model of the actual system, several feedback controllers are developed, each offering different levels of performance depending on the scenario. The RL algorithm is employed to determine and apply the optimal control strategy within a given operational range. Four control methods are investigated: Linear Parameter Varying (LPV), Ultra-local Model-based (ULM), Linear Quadratic Regulator (LQR), and a kinematic model-based controller. The performance and effectiveness of the proposed approach are assessed through three test scenarios using the high-fidelity vehicle simulation platform, CarMaker.

The paper presents a novel observer design method for estimating the front and rear wheel slips of the vehicle. The proposed observer design technique consists of two parts: A simple linear observer algorithm, which uses a reformulated lateral vehicle model to estimate the tire slips. The second part is based on an ultra-local model. The main goal of the ultra-local model is to eliminate the nonlinear, unmodeled, uncertain dynamics of the lateral vehicle model. In this way, the performance level of the linear observer can be significantly increased especially under critical circumstances such as high lateral acceleration maneuvers or driving on a low µ surface. The proposed observer algorithm is implemented in MATLAB/Simulink environment connected to the high-fidelity simulation software, CarMaker. The operation and the effectiveness of the proposed observer are demonstrated through several simulation examples.