Distributed optimal event-triggered cooperative control for nonlinear multi-missile guidance systems with partially unknown dynamics

This article is concerned with the design of a novel distributed optimal event-triggered (ET) cooperative control strategy for nonlinear multi-missile guidance systems under the condition of partially unknown dynamics via adaptive dynamics programming. First, an improved online-identifier is proposed to reconstruct the unknown dynamics based on the data-driven mechanism in which an adaptive compensation term is introduced. The identification residual error is counteracted and the priori identification information is not required. In order to solve the Hamilton-Jacobi-Bellman equation, a single critic neural network (CNN) is utilized to approximate the online solutions and helps calculate the control policy. Then, the uniformly ultimately bounded for the ET closed-loop system and the CNN weight error are proved by utilizing Lyapunov theory. Finally, the application to the multi-missile guidance systems with simultaneous impact consideration validates all missiles can hit the target simultaneously and indicates the effectiveness of the designed approach.