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RISS 인기검색어
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- 저자 : Konghui Guo (검색결과 2 건)
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TIRE WEAR ESTIMATION BASED ON NONLINEAR LATERAL DYNAMIC OF MULTI-AXLE STEERING VEHICLE
Xiang Chen,Nan Xu,Konghui Guo 한국자동차공학회 2018 International journal of automotive technology Vol.19 No.1
This paper presents a novel nonlinear dynamic model of a multi-axle steering vehicle to estimate the lateral wear amount of tires. Firstly, a 3DOF nonlinear vehicle dynamic model is developed, including dynamic models of the hydropneumatic suspension, tire, steering system and toe angle. The tire lateral wear model is then built and integrated into the developed vehicle model. Based on the comparison of experimental and simulation results, the nonlinear model is proved to be better than a linear model for the tire wear calculation. In addition, the effects of different initial toe angles on tire wear are analyzed. As simulation results shown, the impact of the dynamic toe angle on the tire wear is significant. The tire wear amount will be much larger than that caused by normal wear if the initial toe angle increases to 1° - 1.5°. The results also suggest that the proposed nonlinear model is of great importance in the design and optimazation of vehicle parameters in order to reduce the tire wear.
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Linhe Ge,Yang Zhao,Shouren Zhong,Zitong Shan,Fangwu Ma,Zhiwu Han,Konghui Guo 제어·로봇·시스템학회 2023 International Journal of Control, Automation, and Vol.21 No.9
The steady-state error problem of autonomous vehicle MPC-based motion control has not been effectively solved for a long time. This problem is more serious for lateral and longitudinal coupling control problems of vehicles with over-actuated configurations. Based on our newly designed general offset-free MPC (OF-MPC) solver and the TMeasy tire model, a steady-state error free control strategy for simultaneous stability and path following control of four-wheel steering and four-wheel drive vehicles is proposed. OF-MPC uses the disturbances term to describe the model mismatch and external disturbances, then uses the Kalman filter to observe the disturbances, and finally considers the disturbances in the optimization stage to realize the control without steady-state error. Realtime simulation results show that OF-MPC can solve model mismatch and external disturbances problems, and the steady-state error free control is realized. The simulation results of the double lane change maneuver show that the OF-MPC dynamic control performance is also better than the traditional MPC (TRA-MPC), which is more obvious when the vehicle is at the stability boundary and under various constant or time-varying disturbances. Regardless of the dimensions and complex constraints of this problem, real-time performance is still guaranteed, thanks to the proposed OF-MPC. When the horizon length is 100, the average time consumption is only about 15 milliseconds.
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