Event-triggered distributed self-learning robust tracking control for uncertain nonlinear interconnected systems

In this paper, a novel event-triggered self-learning robust tracking control scheme for a class of nonlinear interconnected systems with uncertain interconnections is developed based on adaptive dynamic programming (ADP). Initially, the robust tracking control problem of interconnected systems is transformed to the robust stabilization one of augmented interconnected systems. To address the uncertain interconnections as well as optimize the sample intervals, a group of auxiliary subsystems, each with a novel discounted cost function, are introduced, which enables the robust stabilization problem to be further converted to a group of two-player zero-sum differential games. Next, an event-triggered ADP algorithm is proposed to obtain the saddle point solutions, based on which the distributed event-triggered robust tracking control policies for the overall system are established. Specifically, the proposed event-triggered mechanism is asynchronous and distributed where the sample intervals are optimized to further reduce the computational burden and save the communication resources. Moreover, the tracking errors and the approximation errors of critic network weights are demonstrated to be uniformly ultimately bounded by using the Lyapunov approach. Finally, two simulation examples are provided to verify the effectiveness of the proposed method.