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

Highlights：

• The proposed method extends application scope of ADP theory to the filed of distributed tracking control of interconnected nonlinear systems with uncertain interconnection terms.

• A novel discounted cost function is defined for each auxiliary subsystem which can accommodate the control objectives of guaranteeing the tracking performance, optimizing sample intervals and compensating the unmatched interconnection terms simultaneously.

• The proposed triggering condition for each subsystem is designed by using the estimated worst-case control policy error of auxiliary subsystem, which can maximize the sample intervals to further reduce the computational burden and save the communication resources.

• The event-triggered mechanism designed in this paper operates in an asynchronous and distributed manner which only needs local state information.