Dynamic Event-Triggered Control for Hierarchical Differential Games

This paper proposes a novel dynamic event-triggered control method for a class of completely unknown nonaffine hierarchical differential games, incorporating asymmetric boundaries in both system states and control strategies. To tackle this problem, dynamic feedback and mapping functions are first introduced to construct an unconstrained affine augmented system. Then, integral reinforcement learning techniques are used to derive the Hamilton-Jacobi equation without the original system dynamics. Furthermore, dynamic event-triggered control is employed to alleviate the network transmission burden. During the algorithm implementation, critic neural networks are designed for each agent. Analysis results show that the states and weights are ultimately uniformly bounded. Finally, simulation results using the torsional pendulum system and RLC circuit system validate the effectiveness of the present method.