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

• Published in: IEEE transaction on neural networks and learning systems Vol. 31; no. 3; pp. 972 - 983
• Main Authors: Wang, Huanqing, Liu, Peter Xiaoping, Bao, Jialei, Xie, Xue-Jun, Li, Shuai
• Format: Journal Article
• Language: English
• Published: United States IEEE 01.03.2020; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Adaptive control; Adaptive neural control; Adaptive systems; Artificial neural networks; Backstepping; Computer simulation; Controllers; Decentralized control; Disturbances; Feedback; Feedback control; large-scale systems; Neural networks; Nonlinear control; Nonlinear systems; Observers; Output feedback; Product design; Radial basis function; State observers; stochastic disturbances; Stochastic processes; Stochasticity
• ISSN: 2162-237X; 2162-2388
• DOI: 10.1109/TNNLS.2019.2912082

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.