Research Article | OPEN ACCESS
Robust Fault Detection Algorithm for the Smart Anti-pinch Window of Pure Electric Vehicles
1Hongqiang Li, 1Xiaofei Wang, 1Fangshu Liu, 2Hong Chen and 1Yongqiang Meng
1School of Electronics and Information Engineering, Tianjin Polytechnic University,
Tianjin 300387, China
2China Automotive Technology and Research Center, Tianjin 300162, China
Research Journal of Applied Sciences, Engineering and Technology 2013 24:5683-5693
Received: January 16, 2013 | Accepted: February 18, 2013 | Published: 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.
Keywords:
Anti-pinch window, fault detection, LMI, observer, robustness, sensitivity,
Competing interests
The authors have no competing interests.
Open Access Policy
This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.
Copyright
The authors have no competing interests.
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ISSN (Online): 2040-7467
ISSN (Print): 2040-7459 |
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