Decentralized adaptive delay-dependent neural network control for a class of large-scale interconnected nonlinear systems

This paper investigates the problem of adaptive decentralized control for a class of large-scale interconnected nonlinear systems with unknown time delays, and the unmeasured states. Compared with the existing results, the delay parameters are estimated by utilizing mean value theorem and adaptive mechanism. With the help of the delay estimations, a delay-dependent state observer is designed to make the states available. Based on Lyapunov stability theorem and the backstepping technique, the novel adaptive neural network memory output-feedback controllers are developed. It is proved that the proposed control scheme can guarantee that all the signals in the closed-loop system are bounded and the tracking error converges to the adjustable neighborhood of the origin. The effectiveness of the proposed control scheme is illustrated by the simulation results.