Neural-network-based learning control for the high-speed path tracking of unmanned ground vehicles

In this paper, a neural-network-based learning control method is proposed for the high-speed path tracking of unmanned ground vehicles (UGVs). In our work, a reinforcement-learning controller is combined with a conventional PID controller so that the robustness of PID control and the optimization ability of learning control can both be utilized. The proposed method uses an adaptive-critic learning controller with two outputs to tune the PD parameters online. The architecture of the learning controller includes a critic neural network and two action neural networks and the adaptive-heuristic-critic (AHC) learning algorithm is used to adjust the weights. Simulation results show that the path tracking performance of high-speed vehicles can be improved by the proposed method.