期刊
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
卷 29, 期 1, 页码 144-155出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TNNLS.2016.2616906
关键词
Adaptive decentralized control; backstepping control; graph theory; interconnected nonlinear systems; neural networks (NNs)
类别
资金
- Funds of National Science of China [61273148, 61420106016, 61403070, 61621004]
- Fundamental Research Funds for the Central Universities [N140402002, N150404025]
- China Postdoctoral Science Foundation [2015T80263]
- IAPI Fundamental Research Funds [2013ZCX01-01]
This paper is concerned with the adaptive decentralized fault-tolerant tracking control problem for a class of uncertain interconnected nonlinear systems with unknown strong interconnections. An algebraic graph theory result is introduced to address the considered interconnections. In addition, to achieve the desirable tracking performance, a neural-network-based robust adaptive decentralized fault-tolerant control (FTC) scheme is given to compensate the actuator faults and system uncertainties. Furthermore, via the Lyapunov analysis method, it is proven that all the signals of the resulting closed-loop system are semiglobally bounded, and the tracking errors of each subsystem exponentially converge to a compact set, whose radius is adjustable by choosing different controller design parameters. Finally, the effectiveness and advantages of the proposed FTC approach are illustrated with two simulated examples.
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