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