Adaptive Dynamic Event-Triggered Optimal Control for Vehicle Platoon System under Jointly Connected Switching Topology

Firstly, a novel performance index function is constructed for the platoon system with input saturation. Furthermore, an integral reinforcement learning method is employed to online solve the Hamilton-Jacobi-Bellman equation in the optimal control.

To address the strict requirement of the persistence of excitation condition in integral reinforcement learning, a gradient descent method based on an experience replay mechanism is proposed to update the weights of the fuzzy logic system, thereby relaxing the strict persistence of excitation condition required by traditional integral reinforcement learning. On this basis, a dual dynamic event-triggered mechanism is introduced at both the communication and control ends which can effectively save on-board resources.