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RISS 인기검색어
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- 저자 : Steven X. Ding (검색결과 2 건)
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Adaptive Backstepping Based Fault-tolerant Control for High-speed Trains with Actuator Faults
Shangkun Liu,Bin Jiang,Zehui Mao,Steven X. Ding 제어·로봇·시스템학회 2019 International Journal of Control, Automation, and Vol.17 No.6
In this paper, the fault-tolerant control problem is addressed for high-speed trains with unknown time varying system parameters, traction system actuator faults and disturbances. To represent the unknown time-varying system parameters in mathematical form, a new piecewise time-varying indicator including commonly used 0-1 case, is employed, which results in a piecewise time-varying nonlinear model with some unknown parameters and piecewise disturbances. For the healthy system with the time-varying indicator in the unknown forms and having known switching time instants, an adaptive backstepping controller is proposed to deal with the unknown system parameters and disturbances and achieve the position tracking. Further, the adaptive backstepping based faulttolerant controller with adaptive laws is developed to guarantee the system states boundedness and the position tracking, in the presence of unknown actuator faults. Based on Lyapunov function, the stability of the closed-loop system is proved. Simulation studies on CRH2 (China Railway High-speed Train II) verify the performance of the proposed fault-tolerant control scheme.
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Zhi-kun Hu,Wei-hua Gui,Chun-hua Yang,Peng-cheng Deng,Steven X. Ding 제어·로봇·시스템학회 2011 International Journal of Control, Automation, and Vol.9 No.4
A new classification method for fault waveform is proposed based on discrete orthogonal wavelet transform (DOWT) and hybrid support vector machine (hybrid SVM) for fault type of a three-phase voltage inverter. The waveforms of output voltage obtained from the faulty inverter are decomposed by DOWT into wavelet coefficient matrices, through which we can obtain singular value vectors acted as features of time-series periodic waveforms. And then a multi-classes classification method based on a new Huffman Tree structure is presented to realize 1-v-r SVM strategy. The extracted features are applied to hybrid SVM for determining fault type. Compared to employing the structure based on ordinary binary tree, the superiority of the proposed SVM method is shown in the success of fault diagnosis because the average Loo-correctness of the SVM based on Huffman tree structure exceed the general SVM 3.65%, and the correctness reaches 99.6%.
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