Journal
IEEE TRANSACTIONS ON CYBERNETICS
Volume 44, Issue 11, Pages 2190-2201Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TCYB.2014.2303131
Keywords
Adaptive control; FTC; neural networks (NNs)-based control
Categories
Funding
- National Natural Science Foundation of China [61273171, 61034005, 61174046]
- Australian Research Council [DP140102180]
- National Key Basic Research Program, China [2011CB710706, 2012CB215202]
- 111 Project [B12018]
- Ministry of Education of China [20113218110011]
- State Key Laboratory of Synthetical Automation for Process Industries
- Natural Science Foundation of Jiangsu Province [BK20131364]
- Fundamental Research Funds for the Central Universities [NE2014202]
- Priority Academic Program Development of Jiangsu Higher Education Institutions
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In this paper, the problem of adaptive active fault-tolerant control for a class of nonlinear systems with unknown actuator fault is investigated. The actuator fault is assumed to have no traditional affine appearance of the system state variables and control input. The useful property of the basis function of the radial basis function neural network (NN), which will be used in the design of the fault tolerant controller, is explored. Based on the analysis of the design of normal and passive fault tolerant controllers, by using the implicit function theorem, a novel NN-based active fault-tolerant control scheme with fault alarm is proposed. Comparing with results in the literature, the fault-tolerant control scheme can minimize the time delay between fault occurrence and accommodation that is called the time delay due to fault diagnosis, and reduce the adverse effect on system performance. In addition, the FTC scheme has the advantages of a passive fault-tolerant control scheme as well as the traditional active fault-tolerant control scheme's properties. Furthermore, the fault-tolerant control scheme requires no additional fault detection and isolation model which is necessary in the traditional active fault-tolerant control scheme. Finally, simulation results are presented to demonstrate the efficiency of the developed techniques.
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