State observation of nonlinear off-road vehicle system under complex maneuver condition
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DOI 10.1007/s12206-020-0901-1
Journal of Mechanical Science and Technology 34 (10) 2020 Original Article DOI 10.1007/s12206-020-0901-1 Keywords: · Rolling optimization principle · Vehicle state observation · Moving horizon estimation · Complex maneuver condition
Correspondence to: Hongbin Ren [email protected]
Citation: Gao, Z., Chen, S., Ren, H., Chen, Y., Liu, Z. (2020). State observation of nonlinear off-road vehicle system under complex maneuver condition. Journal of Mechanical Science and Technology 34 (10) (2020) ?~?. http://doi.org/10.1007/s12206-020-0901-1
Received November 7th, 2019 Revised
June 19th, 2020
Accepted August 3rd, 2020 † Recommended by Editor No-cheol Park
State observation of nonlinear off-road vehicle system under complex maneuver condition Zepeng Gao, Sizhong Chen, Hongbin Ren, Yong Chen and Zheng Liu Automotive Research Institute, School of Mechanical Engineering, Beijing Institute of Technology, Beijing 100081, China
Abstract The information of vehicle attitude and tire force under complex environment and maneuver condition is of great significance for system risk prediction and active control system intervention. In order to collect the accurate system states, the coupling vehicle dynamics model and moving horizon estimation method are employed to solve the online optimization problem based on the premise of rolling optimization. Furthermore, the accurate observation and acquisition of the vehicle system state are realized. On this basis, the simulation process of the vehicle state observation using moving horizon estimation method and unscented Kalman filter algorithm are implemented, respectively. The corresponding observation results under complex maneuvering conditions are further validated by using the hardware-in-the-loop experimental platform. Finally, the comparison of the observation results obtained by the unscented Kalman filter and moving horizon estimation algorithms demonstrate that the moving horizon estimation method can effectively improve the observation accuracy of vehicle system state in complex environment, including vehicle roll angle and tire dynamic force. The results obtained through moving horizon estimation method are conducive to the further signal early warning, risk prediction and assessment, as well as systematic intervention and active rollover control. 1. Introduction
© The Korean Society of Mechanical Engineers and Springer-Verlag GmbH Germany, part of Springer Nature 2020
With the rapid development of commercial sensing technology and electronic information field, the vehicle industry has made remarkable progress and leap accordingly [1]. Meanwhile, the drivers and passengers are increasingly demanding for vehicle ride comfort, driving stability, safety and advanced intelligent driving technology [2]. The realization of vehicle superior performance and effective electronic control technology depends on the fast signal transmission and coordinated operation of different vehicle control systems. Through the fast feed-back of sensors and sys
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