Real-time estimation and prediction of lateral stability of coaches: a hybrid approach based on EKF, BPNN, and online autoregressive integrated moving average algorithm

This study aimed to develop a coach state estimation and prediction system to enhance driving safety. Different from existing vehicle stability estimation studies, the authors propose a hybrid method to estimate and predict the state of a coach in real time. First, the vehicle sideslip angle and yaw...

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Veröffentlicht in:	IET intelligent transport systems Jg. 14; H. 13; S. 1892 - 1902
Hauptverfasser:	Fu, Rui, Zhang, Hailun, Guo, Yingshi, Yang, Fei, Lu, Yuping
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	The Institution of Engineering and Technology 15.12.2020
Schlagworte:	autoregressive moving average processes backpropagation boundary function parameter values boundary value parameter database BPNN coach state estimation driving influencing factors driving safety EKF extended Kalman filter estimation algorithm front‐wheel angle hybrid approach hybrid method Kalman filters neural nets nonlinear filters online autoregressive integrated moving average algorithm online prediction prediction system prediction value propagation neural network model Research Article road safety road vehicles seven‐degrees‐of‐freedom vehicle model analyses stability state estimation steering system steering systems steering wheel input torque three‐degrees‐of‐freedom vehicle model time estimation time series vehicle dynamics vehicle sideslip angle rate vehicle stability estimation studies wheels yaw rate wheels coach state estimation yaw rate hybrid method extended Kalman filter estimation algorithm front-wheel angle autoregressive moving average processes vehicle sideslip angle rate steering systems three-degrees-of-freedom vehicle model road safety steering system propagation neural network model BPNN prediction value road vehicles online autoregressive integrated moving average algorithm prediction system Kalman filters stability hybrid approach EKF driving safety vehicle stability estimation studies time series vehicle dynamics nonlinear filters time estimation driving influencing factors online prediction boundary value parameter database steering wheel input torque boundary function parameter values seven-degrees-of-freedom vehicle model analyses backpropagation state estimation neural nets
ISSN:	1751-956X, 1751-9578
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Beschreibung
Zusammenfassung:	This study aimed to develop a coach state estimation and prediction system to enhance driving safety. Different from existing vehicle stability estimation studies, the authors propose a hybrid method to estimate and predict the state of a coach in real time. First, the vehicle sideslip angle and yaw rate are estimated by a three-degrees-of-freedom vehicle model combined with an extended Kalman filter (EKF) estimation algorithm. Then, a steering system is established that replaces the front-wheel angle with the steering wheel input torque. Next, a seven-degrees-of-freedom vehicle model analyses the effects of various driving influencing factors on the vehicle sideslip angle and the boundary of the stable region of the phase plane of the vehicle sideslip angle rate, and a boundary value parameter database is obtained. A back propagation neural network (BPNN) model is then established to obtain the boundary function parameter values under multifactor coupling conditions. Furthermore, an online prediction of the steering wheel input torque in a time series is done, and the prediction value is input to the steering system and neural network model. The effectiveness of the proposed method was evaluated via simulations based on MATLAB/Simulink and TruckSim software.
ISSN:	1751-956X 1751-9578
DOI:	10.1049/iet-its.2020.0385