Decentralized Output-Feedback Neural Control for Systems With Unknown Interconnections

• Published in: IEEE transactions on systems, man and cybernetics. Part B, Cybernetics Vol. 38; no. 1; pp. 258 - 266
• Main Authors: Chen, Weisheng, Li, Junmin
• Format: Journal Article
• Language: English
• Published: United States IEEE 01.02.2008
• Subjects: Adaptive control; Adaptive control systems; Algorithms; Backstepping; Centralized control; Computer Simulation; Control systems; Cybernetics; decentralized control; Distributed control; Feedback; Interconnections; Large-scale systems; Models, Statistical; Neural networks; Neural Networks (Computer); neural networks (NNs); Nonlinear control systems; Nonlinear Dynamics; Programmable control; Reference signals; Reproducibility of Results; Sensitivity and Specificity; Signal design; Tracking errors; Backstepping; decentralized control; neural networks (NNs); large-scale systems
• ISSN: 1083-4419
• DOI: 10.1109/TSMCB.2007.904544

An adaptive backstepping neural-network control approach is extended to a class of large-scale nonlinear output-feedback systems with completely unknown and mismatched interconnections. The novel contribution is to remove the common assumptions on interconnections such as matching condition, bounded by upper bounding functions. Differentiation of the interconnected signals in backstepping design is avoided by replacing the interconnected signals in neural inputs with the reference signals. Furthermore, two kinds of unknown modeling errors are handled by the adaptive technique. All the closed-loop signals are guaranteed to be semiglobally uniformly ultimately bounded, and the tracking errors are proved to converge to a small residual set around the origin. The simulation results illustrate the effectiveness of the control approach proposed in this correspondence.