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Robust Adaptive Fault Reconfiguration for Micro-gas Turbine Based on Optimized T–S Fuzzy Model and Nonsingular TSMO

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Abstract

This paper presents a novel robust fault reconfiguration scheme based on optimized Takagi–Sugeno (T–S) fuzzy model and nonsingular terminal sliding mode observer (NTSMO) with adaptive law for micro-gas turbine (MGS). An optimized T–S fuzzy model is introduced because it can approximate any nonlinear model with arbitrary precision, and an improved imperial competition algorithm (ICA) using adaptive reform probability is proposed to improve the accuracy of the model. A linear transformation method is introduced to decouple the fault and disturbance of the system. The nonsingular terminal sliding mode observer is designed to reconstruct actuator fault and disturbance with unknown upper bound of a change rate, in which an adaptive law is introduced to update the sliding mode gain in real-time to eliminate the influence of fault, disturbance and modeling uncertainty. Simulations in Matlab/Simulink show high reconfiguration accuracy and high-speed of the proposed method despite of the presence of fault, disturbance and modeling uncertainty.

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Acknowledgements

This work is supported by the National Natural Science Foundation of China [Grant Number: 51876089], and the Fundamental Research Funds for the Central Universities [Grant Number: kfjj20190205].

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Authors and Affiliations

  1. College of Energy and Power Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing, 210016, People’s Republic of China

    Leiming Ma, Lingfei Xiao, Zhongxiang Meng & Xinhao Huang

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  1. Leiming Ma
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  2. Lingfei Xiao
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  3. Zhongxiang Meng
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Corresponding author

Correspondence to Lingfei Xiao.

Appendices

Appendices

1.1 Appendix A

See Table 3.

Table 3 Main parameters of MGS
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1.2 Appendix B

See Table 4.

Table 4 Optimization algorithm parameters
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Ma, L., Xiao, L., Meng, Z. et al. Robust Adaptive Fault Reconfiguration for Micro-gas Turbine Based on Optimized T–S Fuzzy Model and Nonsingular TSMO. Int. J. Fuzzy Syst. 22, 2204–2222 (2020). https://doi.org/10.1007/s40815-020-00917-7

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