ZHOU, Lin ;WANG, Guoqiang ;SUN, Kangkang ;LI, Xin . Trajectory Tracking Study of Track Vehicles Based on Model Predictive Control. Strojniški vestnik - Journal of Mechanical Engineering, [S.l.], v. 65, n.6, p. 329-342, june 2019. ISSN 0039-2480. Available at: <https://www.sv-jme.eu/sl/article/trajectory-tracking-study-of-tracked-vehicle-based-on-model-predictive-control/>. Date accessed: 19 nov. 2024. doi:http://dx.doi.org/10.5545/sv-jme.2019.5980.
Zhou, L., Wang, G., Sun, K., & Li, X. (2019). Trajectory Tracking Study of Track Vehicles Based on Model Predictive Control. Strojniški vestnik - Journal of Mechanical Engineering, 65(6), 329-342. doi:http://dx.doi.org/10.5545/sv-jme.2019.5980
@article{sv-jmesv-jme.2019.5980,
author = {Lin Zhou and Guoqiang Wang and Kangkang Sun and Xin Li},
title = {Trajectory Tracking Study of Track Vehicles Based on Model Predictive Control},
journal = {Strojniški vestnik - Journal of Mechanical Engineering},
volume = {65},
number = {6},
year = {2019},
keywords = {model predictive control; trajectory tracking; tracked vehicle; electromechanical coupling},
abstract = {This paper proposes a model predictive control (MPC) algorithm for trajectory tracking of vehicles. Using MPC can reduce tracking errors and random disturbances in complex environments in time. According to the linear kinematics model of the vehicle, a kinematics trajectory tracking controller and an electromechanical coupling dynamics trajectory tracking controller are designed. The drive system of the electrically driven tracked vehicle is non-linear, and an electromagnetic system and mechanical system interact with each other. Taking the electromechanical coupling characteristics into consideration can ensure the matching of the electromechanical performance and the stability of the system during the trajectory tracking control. To verify the algorithm, kinematic simulations and dynamic simulations are performed. The simulation results show that the algorithm has good tracking ability. In addition, a set of test devices is designed to confirm the performance of the trajectory-tracking control algorithm in a real environment. Vision recognition is used to obtain vehicle deviation, and the Kalman filter is used to reduce signal interference. The result shows that the algorithm can meet trajectory tracking requirements.},
issn = {0039-2480}, pages = {329-342}, doi = {10.5545/sv-jme.2019.5980},
url = {https://www.sv-jme.eu/sl/article/trajectory-tracking-study-of-tracked-vehicle-based-on-model-predictive-control/}
}
Zhou, L.,Wang, G.,Sun, K.,Li, X. 2019 June 65. Trajectory Tracking Study of Track Vehicles Based on Model Predictive Control. Strojniški vestnik - Journal of Mechanical Engineering. [Online] 65:6
%A Zhou, Lin %A Wang, Guoqiang %A Sun, Kangkang %A Li, Xin %D 2019 %T Trajectory Tracking Study of Track Vehicles Based on Model Predictive Control %B 2019 %9 model predictive control; trajectory tracking; tracked vehicle; electromechanical coupling %! Trajectory Tracking Study of Track Vehicles Based on Model Predictive Control %K model predictive control; trajectory tracking; tracked vehicle; electromechanical coupling %X This paper proposes a model predictive control (MPC) algorithm for trajectory tracking of vehicles. Using MPC can reduce tracking errors and random disturbances in complex environments in time. According to the linear kinematics model of the vehicle, a kinematics trajectory tracking controller and an electromechanical coupling dynamics trajectory tracking controller are designed. The drive system of the electrically driven tracked vehicle is non-linear, and an electromagnetic system and mechanical system interact with each other. Taking the electromechanical coupling characteristics into consideration can ensure the matching of the electromechanical performance and the stability of the system during the trajectory tracking control. To verify the algorithm, kinematic simulations and dynamic simulations are performed. The simulation results show that the algorithm has good tracking ability. In addition, a set of test devices is designed to confirm the performance of the trajectory-tracking control algorithm in a real environment. Vision recognition is used to obtain vehicle deviation, and the Kalman filter is used to reduce signal interference. The result shows that the algorithm can meet trajectory tracking requirements. %U https://www.sv-jme.eu/sl/article/trajectory-tracking-study-of-tracked-vehicle-based-on-model-predictive-control/ %0 Journal Article %R 10.5545/sv-jme.2019.5980 %& 329 %P 14 %J Strojniški vestnik - Journal of Mechanical Engineering %V 65 %N 6 %@ 0039-2480 %8 2019-06-21 %7 2019-06-21
Zhou, Lin, Guoqiang Wang, Kangkang Sun, & Xin Li. "Trajectory Tracking Study of Track Vehicles Based on Model Predictive Control." Strojniški vestnik - Journal of Mechanical Engineering [Online], 65.6 (2019): 329-342. Web. 19 Nov. 2024
TY - JOUR AU - Zhou, Lin AU - Wang, Guoqiang AU - Sun, Kangkang AU - Li, Xin PY - 2019 TI - Trajectory Tracking Study of Track Vehicles Based on Model Predictive Control JF - Strojniški vestnik - Journal of Mechanical Engineering DO - 10.5545/sv-jme.2019.5980 KW - model predictive control; trajectory tracking; tracked vehicle; electromechanical coupling N2 - This paper proposes a model predictive control (MPC) algorithm for trajectory tracking of vehicles. Using MPC can reduce tracking errors and random disturbances in complex environments in time. According to the linear kinematics model of the vehicle, a kinematics trajectory tracking controller and an electromechanical coupling dynamics trajectory tracking controller are designed. The drive system of the electrically driven tracked vehicle is non-linear, and an electromagnetic system and mechanical system interact with each other. Taking the electromechanical coupling characteristics into consideration can ensure the matching of the electromechanical performance and the stability of the system during the trajectory tracking control. To verify the algorithm, kinematic simulations and dynamic simulations are performed. The simulation results show that the algorithm has good tracking ability. In addition, a set of test devices is designed to confirm the performance of the trajectory-tracking control algorithm in a real environment. Vision recognition is used to obtain vehicle deviation, and the Kalman filter is used to reduce signal interference. The result shows that the algorithm can meet trajectory tracking requirements. UR - https://www.sv-jme.eu/sl/article/trajectory-tracking-study-of-tracked-vehicle-based-on-model-predictive-control/
@article{{sv-jme}{sv-jme.2019.5980},
author = {Zhou, L., Wang, G., Sun, K., Li, X.},
title = {Trajectory Tracking Study of Track Vehicles Based on Model Predictive Control},
journal = {Strojniški vestnik - Journal of Mechanical Engineering},
volume = {65},
number = {6},
year = {2019},
doi = {10.5545/sv-jme.2019.5980},
url = {https://www.sv-jme.eu/sl/article/trajectory-tracking-study-of-tracked-vehicle-based-on-model-predictive-control/}
}
TY - JOUR AU - Zhou, Lin AU - Wang, Guoqiang AU - Sun, Kangkang AU - Li, Xin PY - 2019/06/21 TI - Trajectory Tracking Study of Track Vehicles Based on Model Predictive Control JF - Strojniški vestnik - Journal of Mechanical Engineering; Vol 65, No 6 (2019): Strojniški vestnik - Journal of Mechanical Engineering DO - 10.5545/sv-jme.2019.5980 KW - model predictive control, trajectory tracking, tracked vehicle, electromechanical coupling N2 - This paper proposes a model predictive control (MPC) algorithm for trajectory tracking of vehicles. Using MPC can reduce tracking errors and random disturbances in complex environments in time. According to the linear kinematics model of the vehicle, a kinematics trajectory tracking controller and an electromechanical coupling dynamics trajectory tracking controller are designed. The drive system of the electrically driven tracked vehicle is non-linear, and an electromagnetic system and mechanical system interact with each other. Taking the electromechanical coupling characteristics into consideration can ensure the matching of the electromechanical performance and the stability of the system during the trajectory tracking control. To verify the algorithm, kinematic simulations and dynamic simulations are performed. The simulation results show that the algorithm has good tracking ability. In addition, a set of test devices is designed to confirm the performance of the trajectory-tracking control algorithm in a real environment. Vision recognition is used to obtain vehicle deviation, and the Kalman filter is used to reduce signal interference. The result shows that the algorithm can meet trajectory tracking requirements. UR - https://www.sv-jme.eu/sl/article/trajectory-tracking-study-of-tracked-vehicle-based-on-model-predictive-control/
Zhou, Lin, Wang, Guoqiang, Sun, Kangkang, AND Li, Xin. "Trajectory Tracking Study of Track Vehicles Based on Model Predictive Control" Strojniški vestnik - Journal of Mechanical Engineering [Online], Volume 65 Number 6 (21 June 2019)
Strojniški vestnik - Journal of Mechanical Engineering 65(2019)6, 329-342
© The Authors, CC-BY 4.0 Int. Change in copyright policy from 2022, Jan 1st.
This paper proposes a model predictive control (MPC) algorithm for trajectory tracking of vehicles. Using MPC can reduce tracking errors and random disturbances in complex environments in time. According to the linear kinematics model of the vehicle, a kinematics trajectory tracking controller and an electromechanical coupling dynamics trajectory tracking controller are designed. The drive system of the electrically driven tracked vehicle is non-linear, and an electromagnetic system and mechanical system interact with each other. Taking the electromechanical coupling characteristics into consideration can ensure the matching of the electromechanical performance and the stability of the system during the trajectory tracking control. To verify the algorithm, kinematic simulations and dynamic simulations are performed. The simulation results show that the algorithm has good tracking ability. In addition, a set of test devices is designed to confirm the performance of the trajectory-tracking control algorithm in a real environment. Vision recognition is used to obtain vehicle deviation, and the Kalman filter is used to reduce signal interference. The result shows that the algorithm can meet trajectory tracking requirements.