Decentralized Finite-Time Adaptive Neural Output-Feedback Quantized Control for Switched Nonlinear Large-Scale Delayed Systems

Abstract

This paper considers the problem of decentralized finite-time adaptive neural output-feedback quantized control for a class of switched nonlinear large-scale delayed systems. A switched high-gain quantized state observer is therefore constructed for each subsystem to estimate unavailable system states. Different from the traditional Lyapunov–Krasovskii functional method, multiple Lyapunov–Krasovskii functions are introduced to develop the decentralized adaptive output-feedback control strategy with neural network approximation for the switched nonlinear large-scale delayed systems. Under a category of switching signals with persistent dwell time, all signals in the closed-loop switched system are semi-globally uniformly ultimate bounded. Meanwhile, the tracking errors can remain in a small domain of origin in finite time. Case studies are finally used to illustrate the flexibility and effectiveness of the proposed control approach, including the switched two continuous stirred tank reactor delayed systems.