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

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.

Smart Anti-Pinch Window Simulation Using H-/H∞ Criterion and MOPSO

“…Which shows that the dynamics of the residual signal r depends on the state e in addition to the parameters f, v and n. Another form of residual in Eq. ( 8) is as follows [14]:…”

“…According to Eq. ( 6), to detect the fault, we select the matrices E f , E v , E n , F f , F v and F n as [14]:…”

“…We use H − to show the criterion of the fault effect [13], which should be maximized and used H ∞ to show the disturbance effect. The criterion H − and H ∞ is defined as [14,15]:…”

“…T rf and T rd are the transfers of the fault and the disturbance to the residual signal, respectively. The two scalars β 0 > 0 , γ 0 > 0 [14]. It should be kept in mind that in condition (1), H − is not really the norm.…”

Smart Pinch Detection for Car's Electric Sunroof Based on Estimation and Compensation of System Disturbance

“…The motivation for this approach comes from the idea that the torque rate is less sensitive to motor parameter uncertainty than the torque or the angular velocity [6] . To weaken the impact of the noise on pinch detection, some researchers proposed H∞ filter to address system noise [15][16][17] , and other researchers proposed Kalman filter to deal with system noise [18][19][20][21] . But H∞ filter does not guarantee that the estimation error variance is minimum or in a limited range.…”