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    Abstract
2013 (Vol. 5, Issue: 24)
Article Information:

Robust Fault Detection Algorithm for the Smart Anti-pinch Window of Pure Electric Vehicles

Hongqiang Li, Xiaofei Wang, Fangshu Liu, Hong Chen and Yongqiang Meng
Corresponding Author:  Hongqiang Li 

Key words:  Anti-pinch window, fault detection, LMI, observer, robustness, sensitivity,
Vol. 5 , (24): 5683-5693
Submitted Accepted Published
January 16, 2013 February 18, 2013 May 30, 2013
Abstract:

In order to effectively solve the risk of safety on power window, an improved pinch detection algorithm based on the fault detection observer estimation is proposed for an anti-pinch window control system. In designing a residual generator, the proposed fault detection algorithm makes use of the pinch torque rate information by establishing the mathematical model of DC, considered as a fault under the pinched condition. By comparing the residual signal with the pre-designed threshold, the occurrence of pinch is detected. The fault detection observer takes into account robustness against disturbances and sensitivity to faults, simultaneously, both of which are regarded as optimization problems. In this study, the mixed H-/H∞ performance index and reference model fault detection method are advanced to solve the optimization problem in the Linear Matrix Inequality (LMI) which transforms a mathematical problem. The simulation results of the detection time obtained from the two methods are 0.15 and 0.07s, respectively, proving that the use of the fault detection algorithm is effective for an anti-pinch window. The co-simulation based on CANoe-MATLAB is proposed to verify the algorithm again. Moreover, under the premise of strong robustness, the reference model method is superior to the mixed H-/H∞ performance.
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  Cite this Reference:
Hongqiang Li, Xiaofei Wang, Fangshu Liu, Hong Chen and Yongqiang Meng, 2013. Robust Fault Detection Algorithm for the Smart Anti-pinch Window of Pure Electric Vehicles.  Research Journal of Applied Sciences, Engineering and Technology, 5(24): 5683-5693.
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ISSN (Online):  2040-7467
ISSN (Print):   2040-7459
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