Dual Closed-Loop Control of Intelligent Vehicles Trajectory Tracking Based on Steering Response Characteristics
-
摘要: 智能车辆轨迹跟踪的准确性与鲁棒性是车辆运动控制性能的重要表征，基于路径预瞄信息的跟踪控制研究使车辆性能显著提升. 然而，车辆转向系统响应不足给车辆实时准确的基于预瞄信息跟踪参考轨迹带来挑战. 针对此问题，实时引入转向系统状态建立双闭环轨迹跟踪控制结构，保证智能车辆轨迹跟踪控制算法对转向系统响应不足的鲁棒性. 具体结构外环基于预瞄信息使用模型预测控制求解最优转向角，内环基于转向状态误差使用PID方法设计反馈控制律以补偿转向响应不足. 双闭环结构耦合控制输入保证了车辆鲁棒最优跟踪控制. 最后通过Carsim与Simulink联合仿真，验证了该双闭环控制结构的有效性.Abstract: The accuracy and robustness of intelligent vehicle trajectory tracking are important characteristic of vehicle motion control performance. Many Researches of tracking control based on path preview information have significantly improved vehicle performance. However, the insufficient response of the vehicle steering system have caused many difficulties for the vehicle to accurately track the reference trajectory based on the preview information in real time. To solve this problem, introducing state of the steering system in real time, a dual closed-loop trajectory tracking control structure was established to ensure the robustness of the intelligent vehicle trajectory tracking control algorithm for the insufficient response of the steering system. Concretely, in the outer loop of this structure, a model predictive control method was used to solve the optimal steering angle based on preview information. And in the inner loop of this structure, a PID method was used to design a feedback control law based on steering state error to compensate for insufficient steering response. Coupling control input, the dual closed-loop structure can ensure the robust optimal tracking control of the vehicle. Finally, the effectiveness of the dual closed-loop control structure was verified in Carsim and Simulink co-simulation.
-
表 1 传统MPC＆双闭环控制误差
Table 1. Traditional MPC & dual closed loop control rms value
算法 横向误差/m 前轮转角误差
/(°)传统MPC 0.826 0.212 双闭环控制 0.221 0.118 
下载: 导出CSV
表 2 横向误差均方根值对比
Table 2. Comparison of RMS Value of Lateral Error
转向响应特性 MPC 双闭环 0.9 0.627 0.097 0.925 0.545 0.081 0.95 0.453 0.083 0.975 0.364 0.060 1 0.273 0.062
下载: 导出CSV
-
[1] 吕文杰, 马戎, 李岁劳, 等. 基于纯追踪模型的路径跟踪改进算法[J]. 测控技术, 2011, 30(7):5. doi: 10.3969/j.issn.1000-8829.2011.07.002LÜ Wenjie, MA Rong, LI suilao, et al. An improved algorithm based on pure pursuit model for path tracking[J]. Measurement & Control Technology, 2011, 30(7):5. (in Chinese) doi: 10.3969/j.issn.1000-8829.2011.07.002 [2] SNIDER J M. Automatic steering methods for autonomous automobile path tracking[R]. Pittsburgh: Carnegie Mellon University Pittsburgh PA Robotics Institute, CMURITR-09-08, 2009. [3] SIAMPIS Efstathios, VELENIS Efstathios, GARIUOLO Salvatore, et al. A real-time nonlinear model predictive control strategy for stabilization of an electric vehicle at the limits of handling[J]. IEEE Transactions on Control Systems Technology, 2018, 26(6):1982 − 1994. [4] STEPHEN Erlien M, SUSUMU Fujita, JOSEPH Christian Gerdes, et al. Shared steering control using safe envelopes for obstacle avoidance and vehicle stability[J]. IEEE Transactions on Intelligent Transportation Systems, 2016, 17(2):441 − 451. [5] GUO Hongyan, SHEN Chen, ZHANG Hui. Simultaneous trajectory planning and tracking using an MPC method for cyberphysical systems: a case study of obstacle avoidance for an intelligent vehicle[J]. IEEE Transactions on Industrial Informatics, 2018, 14(9):4273 − 4283. [6] SCHWARTING Wilko, ALONSO Mora Javier, LIAM Paull, et al. Safe nonlinear trajectory generation for parallel autonomy with a dynamic vehicle model[J]. IEEE Transactions on Intelligent Transportation Systems, 2018, 19(9):2994 − 3008. [7] 刘凯, 王威, 龚建伟, 等. 越野地形下智能车辆的动力学建模与轨迹跟踪[J]. 北京理工大学学报, 2019, 39(9):933 − 937.LIU Kai, WANG Wei, GONG Jianwei et al. Dynamic modeling and trajectory tracking of intelligent vehicles in off-road terrain[J]. Transactions of Beijing Institute of Technology, 2019, 39(9):933 − 937. (in Chinese) [8] 陈特, 徐兴, 蔡英凤, 等. 基于状态估计的无人车前轮转角与横摆稳定协调控制[J]. 北京理工大学学报, 2021, 41(10):1050 − 1057.CHEN Te, XU Xing, CAI Yingfeng, et al. Coordinated control of front-wheel steering angle and yaw stability for unmanned ground vehicle based on state estimation[J]. Transactions of Beijing Institute of Technology, 2021, 41(10):1050 − 1057. (in Chinese) [9] BAI G, MENG Y, LIU L, et al. Review and comparison of path tracking based on model predictive control[J]. Electronics, 2019, 8(10):1077. doi: 10.3390/electronics8101077 [10] KIM E, KIM J, SUN Woo M. Model predictive control strategy for smooth path tracking of autonomous vehicles with steering actuator dynamics[J]. International Journal of Automotive Technology, 2014, 15(7):1155 − 1164. [11] CHEN Shen, GUO Hongyan, LIU Feng, et al. MPC-based path tracking controller design for autonomous ground vehicles[C]//Proceedings of 2017 36th Chinese Control Conference (CCC), Dalian: [s. n.], 2017, 9584 − 9589. doi: 10.23919/ChiCC.2017.8028887. [12] CHEN Yimin, HU Chuan, WANG Junmin. Human-centered trajectory tracking control for autonomous vehicles with driver cut-in behavior prediction[J]. IEEE Trans. Vehicular Technology, 2019, 68(9):8461 − 8471. doi: 10.1109/TVT.2019.2927242 [13] CAO Jingwei, SONG Chuanxue, PENG Silun, et al. Trajectory tracking control algorithm for autonomous vehicle considering cornering characteristics[J]. IEEE Access, 2020(8):59470 − 59484. [14] TANG Luqi, YAN Fuwu, ZOU Bin, et al. An improved kinematic model predictive control for high-speed path tracking of autonomous vehicles[J]. IEEE Access, 2020, 8:51400 − 51413. doi: 10.1109/ACCESS.2020.2980188 [15] TAN Qifan, WANG Xu, JAVAD TAGHIA, et al. Force control of two-wheel-steer four-wheel-drive vehicles using model predictive control and sequential quadratic programming for improved path tracking[J]. International Journal of Advanced Robotic Systems, 2017, 11:1 − 15. doi: 10.1177/1729881417746295 [16] NAM Hyunsik, CHOI Wansik, AHN Changsun. Model predictive control for evasive steering of an autonomous vehicle[J]. International Journal of Automotive Technology, 2019, 20(5):1033 − 1042. -
下载:
点击查看大图
图(5) / 表(2)
计量
- 文章访问数: 83
- HTML全文浏览量: 107
- PDF下载量: 0
- 被引次数: 0
京公网安备 11010502036328号 京ICP备17033152号