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In this paper, an event-triggered Reinforcement Learning (RL) method isproposed for the optimal attitude consensus of multiple rigid body networkswith unknown dynamics. Firstly, the consensus error is constructed through theattitude dynamics. According to the Bellman optimality principle, the implicitform of the optimal controller and the corresponding Hamilton-Jacobi-Bellman(HJB) equations are obtained. Because of the augmented system, the optimalcontroller can be obtained directly without relying on the system dynamics.Secondly, the self-triggered mechanism is applied to reduce the computing andcommunication burden when updating the controller. In order to address theproblem that the HJB equations are difficult to solve analytically, a RL methodwhich only requires measurement data at the event-triggered instants isproposed. For each agent, only one neural network is designed to approximatethe optimal value function. Each neural network is updated only at the eventtriggered instants. Meanwhile, the Uniformly Ultimately Bounded (UUB) of theclosed-loop system is obtained, and the Zeno behavior is also avoided. Finally,the simulation results on a multiple rigid body network demonstrate thevalidity of the proposed method.

Event-Triggered Optimal Attitude Consensus of Multiple Rigid Body Networks with Unknown Dynamics