Affiliation:
1. School of Mechanical and Automotive Engineering, Shanghai University of Engineering Science, Shanghai 201620, China
Abstract
In order to reduce the lateral error of path-following control of unmanned vehicles under variable curvature paths, we propose a path-following control strategy for unmanned vehicles based on optimal preview time model predictive control (OP-MPC). The strategy includes the longitudinal speed limit, the optimal preview time surface, and the model predictive control (MPC)controller. The longitudinal speed limit controls speed to prevent vehicle rollover and sideslip. The optimal preview time surface adjusts the preview time according to the vehicle speed and path curvature. The preview point determined by the preview time is used as the reference waypoint of OP-MPC controller. Finally, the effectiveness of the strategy was verified through simulation and with the real unmanned vehicle. The maximum lateral deviation obtained by the OP-MPC controller was reduced from 0.522 m to 0.145 m under the simulation compared with an MPC controller. The maximum lateral deviation obtained by the OP-MPC controller was reduced from 0.5185 m to 0.2298 m under the real unmanned vehicle compared with the MPC controller.
Reference19 articles.
1. Users’ resistance towards radical innovations: The case of the self-driving car;Neumayr;Transp. Res. Part F Traffic Psychol. Behav.,2017
2. A survey of motion planning and control techniques for self-driving urban vehicles;Paden;IEEE Trans. Intell. Veh.,2016
3. Dudziak, A., Stoma, M., Kuranc, A., and Caban, J. (2021). Assessment of Social Acceptance for Autonomous Vehicles in Southeastern Poland. Energies, 14.
4. Development of a new integrated local trajectory planning and tracking control framework for autonomous ground vehicles;Li;Mech. Syst. Signal Process.,2017
5. Robust trajectory tracking error model-based predictive control for unmanned ground vehicles;Kayacan;IEEE/ASME Trans. Mechatron.,2015