Adaptive Optimal Control for Suppressing Vehicle Longitudinal Vibrations

Hao, Donghao; Zhao, Changlu; Huang, Ying; Yang, Liu; Zhu, Guoming

doi:10.23919/acc.2019.8815221

Cited by 6 publications

(4 citation statements)

References 6 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…where, F x is the wheel longitudinal force(N), F z is the wheel vertical force (N), B x , C x , D x , E x are the parameters representing road characteristic, and are derived from real vehicle experiments, 29 B x = 10.5, C x = 2.51, D x = 1.26, E x = 1, s t represents wheel slip ratio and is calculated by equation (7).…”

Section: Tire Suspension and Vehicle Body Dynamic Modelsmentioning

confidence: 99%

See 1 more Smart Citation

Model predictive control for active vibration suppression of hybrid electric vehicles during mode transition

Huang

Wang

Yue

et al. 2022

Proceedings of the Institution of Mechanical Engineers, Part D:

View full text Add to dashboard Cite

To realize a rapid and comfortable mode transition process from pure electric mode to hybrid mode (E-H), this study proposes an innovative control strategy which combines open-loop control with model predictive control to regulate the E-H process for a P2.5 configuration hybrid electric vehicle. Firstly, a detailed vehicle longitudinal dynamic model is established and the mode transition process is divided into four phases to reveal the control problems. Based on this model, a strategy combining open-loop control with model predictive control is developed. The open-loop control is adopted before the clutch is locked in order to speed up the transition process and limit the vehicle jerk. The model predictive control (MPC) is adopted after the clutch is locked to actively suppress the vibration caused by the abrupt change of clutch torque at the moment of clutch lock-up. Finally, simulation and hardware-in-the-loop test demonstrate that the proposed strategy for mode transition can achieve both switching rapidity and riding comfort. The algorithm robustness is also discussed and the signal transmission delay influence caused by controller area network (CAN) is studied.

show abstract

Section: Tire Suspension and Vehicle Body Dynamic Modelsmentioning

confidence: 99%

“…Thus, the mode transition time should be controlled relatively short. [7][8][9][10] So riding comfort and rapidity are two indicators for E-H process.…”

Section: Introductionmentioning

confidence: 99%

Model predictive control for active vibration suppression of hybrid electric vehicles during mode transition

Huang

Wang

Yue

et al. 2022

Proceedings of the Institution of Mechanical Engineers, Part D:

View full text Add to dashboard Cite

show abstract

“…Therefore, the designed controller should adapt to different road surfaces. In addition to updating the control-oriented model according to changing c t , 25 control design parameters such as weighting coefficients need also to be adjusted as functions of road interaction characteristics.…”

Section: Linearization and Discretizationmentioning

confidence: 99%

“…To maintain the same control performance in different road surfaces, c t needs to be estimated in real-time, and the control design model shall be updated accordantly. The estimated method and results of estimating c t can refer to our early work 25 and is not discussed in this paper.…”

Section: Linear Quadratic Tracking Controlmentioning

confidence: 99%

Model reference adaptive LQT control for anti-jerk utilizing tire-road interaction characteristics

Yue

Huang

Hao

et al. 2020

Proceedings of the Institution of Mechanical Engineers, Part D:

Self Cite

View full text Add to dashboard Cite

Sudden vehicle propulsion torque change under tip-in/out maneuver often leads to low-frequency longitudinal vibration due to the flexibility in the half-shaft and tire slip, which greatly affects vehicle drivability. Note that the vibration frequency is between 1 and 10 Hz and is difficult to be absorbed by the vehicle mechanical system. To optimize the vehicle drivability under tip-in maneuver, an Adaptive Linear Quadratic Tracking (ALQT) anti-jerk traction controller is proposed in this paper. Based on the experimental data, a Carsim-Simulink co-simulation model is developed for assessing control performance. A control-oriented model, considering the nonlinear characteristics of the tire-road friction coefficient and slip ratio, is then proposed. A reference model with rigid axle is used to provide the equilibrium points and reference velocity trajectory. Jacobi linearization method is then used to linearize the model along the desired trajectory and a linear deviation model based on equilibrium points is obtained. Finally, the deviation compensation receding horizon LQT controller is designed along with the Kalman state estimation. The effectiveness of the designed controller is assessed via simulation studies under different road surfaces and compared with PID and LQR controllers. The LQT controller is able to track the desired velocity profile with minimum jerk while increasing road safety. Furthermore, the effect of LQT weighting coefficients under different road surfaces are discussed. Simulation results show that the ALQT controller is able to optimize vehicle drivability under different road surfaces and the weighting matrices shall be selected based on the road condition for optimal drivability.

show abstract