Discrete-Time Model Predictive Control for Lateral Trajectory Tracking of Intelligent Vehicles
GAO Hong-Bo1,2 LI Sheng-Bo1 XIE Guo-Tao3 CHENG Bo1
1. State Key Laboratory of Automotive Safety and Energy, Tsinghua University, Beijing 100084, China
2. Center for Intelligent Connected Vehicles and Transportation, Tsinghua University, Beijing 100084, China
3. Department of Automotive Engineering, Hunan University, Changsha Hunan 410082, China
Abstract:Intellectualization is one of the three transformation technologies of automobile, which is gradually changing the mode of travel and tra±c in human society. Desirable controllability and intelligence is the premise of self-driving cars on the road. This paper presents a discrete-time model predictive control method for lateral trajectory tracking of intelligent vehicles. The lateral trajectory tracking is first transformed into an open-loop optimal control problem. The objective function and input-output constraints are then designed. The lateral tracking predictive controller is developed finally by using the receding horizon optimization. The simulation results show that the average lateral error is 25 cm under the given reference trajectory conditions. Under the condition of tracking stability, the front wheel angle error converges to zero, which has good global stability. The results show that this method has high accuracy, strong robustness and adaptability to real tra±c scenarios, which meets the requirements of lateral motion control in autonomous driving.
高洪波, 李升波,谢国涛,成波. 智能汽车横向轨迹跟踪的离散时间模型预测控制[J]. 指挥与控制学报, 2018, 4(4): 297-305.
GAO Hong-Bo, LI Sheng-Bo, XIE Guo-Tao, CHENG Bo. Discrete-Time Model Predictive Control for Lateral Trajectory Tracking of Intelligent Vehicles. Journal of Command and Control, 2018, 4(4): 297-305.