Distributed optimal consensus control for nonlinear multi-agent systems with input saturation based on event-triggered adaptive dynamic programming method

In this paper, the distributed optimal consensus problem is investigated for a class of continuous-time nonlinear multi-agent systems with input saturation. Non-quadratic cost functions are introduced to handle input constraints and a novel distributed optimal consensus protocol is derived based on event-triggered adaptive dynamic programming method. An online implement scheme is designed under actor-critic network framework in order to obtain the solutions of Hamilton-Jacobi-Bellman equations online. The computation and communication loads are effectively reduced since the weight estimation vectors and controllers are updated only at event-triggered instants. Detailed analysis is presented based on Lyapunov stability theory which guarantees that the weight estimation errors and local consensus errors are uniformly ultimately bounded. Furthermore, it proves that Zeno behaviour can be effectively avoided. Finally, the simulation examples are presented to validate the proposed strategy.