Adaptive Neural Output-Feedback Decentralized Control for Large-Scale Nonlinear Systems With Stochastic Disturbances

TLDR

The paper tackles adaptive neural output‑feedback decentralized control for strongly interconnected nonlinear systems under stochastic disturbances. An observer‑based adaptive backstepping controller is constructed using radial‑basis‑function neural networks to model uncertainties and approximate unmeasurable states. The control scheme guarantees semiglobal uniform ultimate boundedness of all closed‑loop signals in the fourth moment, and simulations confirm its effectiveness.

Abstract

This paper addresses the problem of adaptive neural output-feedback decentralized control for a class of strongly interconnected nonlinear systems suffering stochastic disturbances. An state observer is designed to approximate the unmeasurable state signals. Using the approximation capability of radial basis function neural networks (NNs) and employing classic adaptive control strategy, an observer-based adaptive backstepping decentralized controller is developed. In the control design process, NNs are applied to model the uncertain nonlinear functions, and adaptive control and backstepping are combined to construct the controller. The developed control scheme can guarantee that all signals in the closed-loop systems are semiglobally uniformly ultimately bounded in fourth-moment. The simulation results demonstrate the effectiveness of the presented control scheme.