TY - GEN
T1 - Tracking Control of Unmanned Tracked Vehicle in Off-road Conditions with Large Curvature ∗
AU - Ziye, Zhao
AU - Haiou, Liu
AU - Huiyan, Chen
AU - Shaohang, Xu
AU - Wenli, Liang
N1 - Publisher Copyright:
© 2019 IEEE.
PY - 2019/10
Y1 - 2019/10
N2 - In order to achieve the accurate trajectory tracking for a high-speed tracked vehicle in off-road conditions in the case where it is known that the desired trajectory is a large curvature curve, new research directions are being developed. In the framework of the Model Predictive Control (MPC) algorithm, the simplified ideal kinematics model of the tracked vehicle that ignores the sliding steering characteristics is applied to reduce the iterative solution time under high-speed driving conditions. By adjusting the weight coefficients of the objective function, the trajectory tracking accuracy is improved. This research is based on an unmanned electric drive tracked vehicle and carries out simulation experiments. Through real vehicle experiment, the control sequence of the experienced driver under the certain scene is collected, and then the vehicle control experience of human is obtained. Through simulation experiment, the vehicle tracking errors under different MPC weight coefficients of different curvature curves are obtained. In addition, the tracking control sequence is compared with the driver's control data. By the data analysis, the sensitivity of each weight coefficient to the tracking accuracy and how to create a driving mode that is closer to human by adjusting the weighting coefficients are determined.
AB - In order to achieve the accurate trajectory tracking for a high-speed tracked vehicle in off-road conditions in the case where it is known that the desired trajectory is a large curvature curve, new research directions are being developed. In the framework of the Model Predictive Control (MPC) algorithm, the simplified ideal kinematics model of the tracked vehicle that ignores the sliding steering characteristics is applied to reduce the iterative solution time under high-speed driving conditions. By adjusting the weight coefficients of the objective function, the trajectory tracking accuracy is improved. This research is based on an unmanned electric drive tracked vehicle and carries out simulation experiments. Through real vehicle experiment, the control sequence of the experienced driver under the certain scene is collected, and then the vehicle control experience of human is obtained. Through simulation experiment, the vehicle tracking errors under different MPC weight coefficients of different curvature curves are obtained. In addition, the tracking control sequence is compared with the driver's control data. By the data analysis, the sensitivity of each weight coefficient to the tracking accuracy and how to create a driving mode that is closer to human by adjusting the weighting coefficients are determined.
UR - http://www.scopus.com/inward/record.url?scp=85076812384&partnerID=8YFLogxK
U2 - 10.1109/ITSC.2019.8917468
DO - 10.1109/ITSC.2019.8917468
M3 - Conference contribution
AN - SCOPUS:85076812384
T3 - 2019 IEEE Intelligent Transportation Systems Conference, ITSC 2019
SP - 3867
EP - 3873
BT - 2019 IEEE Intelligent Transportation Systems Conference, ITSC 2019
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2019 IEEE Intelligent Transportation Systems Conference, ITSC 2019
Y2 - 27 October 2019 through 30 October 2019
ER -