Fault-Tolerant Multiplayer Tracking Control for Autonomous Vehicle via Model-Free Adaptive Dynamic Programming

This article investigates the completely unknown autonomous vehicle tracking issues with actuator faults through model-free adaptive dynamic programming (MFADP) approaches. Because partial parameters are measured difficultly or inaccurately, the model-based control theories are imperfect for the vehicles. Therefore, the proposed multiplayer optimal control method in this work, which is not necessary to know the prior system knowledge, achieves the purpose of unknown vehicle tracking control via a novel MFADP theory. Besides, the control strategies are robust, which contain adaptive regulators to eliminate the disturbance of the vehicle systems caused by actuator faults, modeling errors, and curvature interference. To reduce the computational burden of control, a single neural network (NN) architecture is constructed with minimal computational cost and fast response speed. In addition, the convergence analysis of the NN structure, the stability and robustness analysis of identification, and the control schemes in this work are supplied. Finally, two driving scenario simulations are shown to prove the effectiveness of the established controller.