Event-driven intelligent cooperative control of unmanned autonomous systems in complex environment

Abstract

This paper addresses the event-driven cooperative tracking control problem of unmanned autonomous systems with unknown dead zones, unknown disturbances, and output constraints, which are induced by complex environment. The nonlinear functions of system model are estimated by using the neural networks. Furthermore, a state transformation technique is presented to solve the problem of the constrained control. Unlike most existing methods that only consider unilateral factor, the intelligent cooperative control method designed in this paper can deal with multiple factors in complex environment at the same time, and make the unmanned autonomous systems save more energy and improve endurance through event-driven mechanism. Detailed analysis is given based on Lyapunov stability theory which guarantees that the consensus tracking errors are uniformly ultimately bounded. Finally, results of a numerical example and a practical example of marine surface vehicles are shown to verify the effectiveness of the proposed intelligent control protocol.