Dynamic Self-Triggered Intelligent Path Tracking Control for Autonomous Agricultural Vehicles via Reinforcement Learning

This paper investigates the path tracking control of unmanned agricultural vehicles with disturbances under a dynamic self-triggered mechanism via reinforcement learning (RL). To begin with, a path tracking offset system is constructed based on the kinematic model of the unmanned agricultural vehicle, which transforms the path tracking control problem into an optimal control problem. Subsequently, a novel dynamic self-triggered second-order integral sliding mode control policy is developed to mitigate the impact of disturbances and to derive a nominal path tracking offset model. Afterward, to further alleviate the computing and communication burdens, a novel dynamic self-triggered mechanism is proposed for the optimal control policy. It can predict the next update time based on current information, thus avoiding the need for continuous monitoring of the triggering condition. Furthermore, a single critic network architecture is constructed to obtain an approximate path tracking control policy, and it is proven by Lyapunov stability theory that this policy ensures unmanned agricultural vehicles can maintain the predefined working path in the presence of disturbances. Finally, the effectiveness of the proposed path tracking control method is demonstrated by simulation experiments.