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Semi-trailer State Estimation Based on Double Adaptive Unscented Kalman Filter

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    Aiming at the problems of uncertain measurement noise,serious influence of accumulated error and sensitive initial value in the process of semi-trailer state estimation,a double adaptive unscented Kalman filter algo? rithm(FFUKF)is proposed,which is suitable for estimating several state variables of semi-trailer such as hinge angle and vehicle speed. Based on the established 12-degree-of-freedom nonlinear dynamic model and tire model of semi-trailer car,through the measured wheel speed and vehicle acceleration and other information,firstly,fuzzy control is used to adaptively adjust the slip rate tolerance,comprehensively judge the stable state of each wheel,and estimate a vehicle speed through the wheel speed;At the same time,fuzzy control adaptively adjusts the measurement noise,and estimates the hinge angle and another vehicle speed according to dynamics by using unscented Kal? man algorithm;Then,Kalman filtering algorithm is used to fuse the estimation results of the two methods,so as to realize the real-time estimation of the longitudinal and lateral velocity,yaw rate and the articulation angle between trailer and tractor. At last,the multi-condition simulation experiment is carried out in Simulink/TruckSim cosimulation environment,which proves that the proposed double adaptive unscented Kalman estimation algorithm (FFUKF)has strong adaptability,stability and robustness,has higher estimation accuracy than the ordinary fuzzy adaptive unscented Kalman(FUKF),and can effectively overcome the cumulative error. Even if the initial estima? tion value is inaccurate and ABS control input is available,the vehicle speed and hinge angle can still be accurately estimated in real time.

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  • Online: March 04,2022
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