Adaptive neural decentralized output-feedback control for nonlinear large-scale systems with input time-varying delay and saturation

Abstract

For a class of nonstrict-feedback nonlinear large-scale systems with input delay, saturation and unknown virtual control gains, we propose a decentralized output-feedback control scheme in this paper. In the process of control design, a novel auxiliary system is proposed to overcome the difficulty caused by time-varying input delay. The convex combination technique is used to overcome the difficulty caused by unknown virtual control gains, and further construct the state observer. Then a decentralized output-feedback controller is designed by using the adaptive neural backstepping control approach. By Lyapunov stability theory, it is proved that the proposed decentralized output-feedback controller can ensure that tracking errors converge to a small neighborhood of the origin and other closed-loop states are bounded. Finally, the results of simulation example further verify the validity of this decentralized output-feedback control scheme.