TY - GEN
T1 - Adaptive optimal control for suppressing vehicle longitudinal vibrations
AU - Hao, Donghao
AU - Zhao, Changlu
AU - Huang, Ying
AU - Yang, Long
AU - Zhu, Guoming G.
N1 - Publisher Copyright:
© 2019 American Automatic Control Council.
PY - 2019/7
Y1 - 2019/7
N2 - Sudden torque change under the tip-in operation often causes driveline low-frequency torsional vibrations, which seriously impacts vehicle drivability. Typical driveline resonance frequency is under 10Hz in the longitudinal direction and it cannot be eliminated through mechanical design optimization. To provide a smooth acceleration with minimal vibrations, an adaptive optimal tracking controller of engine torque is designed in this paper. A nonlinear model, elaborating the driveline and vehicle longitudinal dynamics, is developed. Based on the linearized control-oriented model, a receding horizon linear quadratic tracking (RHLQT) controller is designed along with the Kalman optimal state estimation. The optimal control design parameters (weightings) are tuned under different road conditions. In addition, the road surface contact friction coefficient is estimated using the recursive Least-Squares method. The RHLQT adapts to the estimated road condition (surface friction). The control performance of the adaptive RHLQT is studied under different road conditions, compared with fixed control parameters LQT controllers. The simulation results confirm the effectiveness of the proposed control scheme.
AB - Sudden torque change under the tip-in operation often causes driveline low-frequency torsional vibrations, which seriously impacts vehicle drivability. Typical driveline resonance frequency is under 10Hz in the longitudinal direction and it cannot be eliminated through mechanical design optimization. To provide a smooth acceleration with minimal vibrations, an adaptive optimal tracking controller of engine torque is designed in this paper. A nonlinear model, elaborating the driveline and vehicle longitudinal dynamics, is developed. Based on the linearized control-oriented model, a receding horizon linear quadratic tracking (RHLQT) controller is designed along with the Kalman optimal state estimation. The optimal control design parameters (weightings) are tuned under different road conditions. In addition, the road surface contact friction coefficient is estimated using the recursive Least-Squares method. The RHLQT adapts to the estimated road condition (surface friction). The control performance of the adaptive RHLQT is studied under different road conditions, compared with fixed control parameters LQT controllers. The simulation results confirm the effectiveness of the proposed control scheme.
UR - http://www.scopus.com/inward/record.url?scp=85072273712&partnerID=8YFLogxK
U2 - 10.23919/acc.2019.8815221
DO - 10.23919/acc.2019.8815221
M3 - Conference contribution
AN - SCOPUS:85072273712
T3 - Proceedings of the American Control Conference
SP - 1736
EP - 1741
BT - 2019 American Control Conference, ACC 2019
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2019 American Control Conference, ACC 2019
Y2 - 10 July 2019 through 12 July 2019
ER -