Path Tracking Control of Vehicles Based on Variable Prediction Horizon and Velocity

被引：11

作者：

Bai G. ^{[1
]}

Meng Y. ^{[1
,2
]}

Liu L. ^{[1
]}

Gu Q. ^{[1
]}

Luo W. ^{[1
]}

Gan X. ^{[1
]}

机构：

[1] School of Mechanical Engineering, University of Science and Technology Beijing, Beijing

[2] Institute of Artificial Intelligence, University of Science and Technology Beijing, Beijing

来源：

Zhongguo Jixie Gongcheng/China Mechanical Engineering | 2020年 / 31卷 / 11期

关键词：

Nonlinear model predictive control(NMPC); Path tracking; Prediction horizon; Velocity;

D O I：

10.3969/j.issn.1004-132X.2020.11.003

中图分类号：

学科分类号：

摘要：

NMPC was widely applied to the path tracking control of vehicles. However, the impacts of prediction horizon and velocity on the performances of path tracking control were not considered in reported researches. Thus, the relationship among prediction horizon, velocity and the performances of path tracking control was analyzed. Through the cubic polynomial fitting, the control laws of the optimal prediction horizon and reference velocity were obtained, which may guarantee the lateral error of path tracking less than 0.1 m. Then, the NMPC controller for path tracking control was improved, and the performances of the NMPC controller were vertified by simulation. The simulation results show that, for the improved NMPC controller, the lateral error is within 0.092 8 m and the heading error is within 0.072 4 rad. Compared with the traditional NMPC controller, the improved NMPC controller reduces the maximum lateral error by more than 4.267 1 m and reduces the maximum heading error by more than 0.392 7 rad, and the performances of path tracking control are improved. © 2020, China Mechanical Engineering Magazine Office. All right reserved.

引用

页码：1277 / 1284

页数：7

共 20 条

[1] JI X, LIU Y, HE X, Et al., Interactive Control Paradigm Based Robust Lateral Stability Controller Design for Autonomous Automobile Path Tracking with Uncertain Disturbance: a Dynamic Game Approach, IEEE Transactions on Vehicular Technology, 67, 8, pp. 6906-6920, (2018)
[2] CHEEIN F A., Intelligent Sampling Technique for Path Tracking Controllers, IEEE Transactions on Control Systems Technology, 24, 2, pp. 747-755, (2016)
[3] CHEN Huiyan, ZHANG Yu, An Overview of Research on Military Unmanned Ground Vehicles, Acta Armamentarii, 35, 10, pp. 1696-1706, (2014)
[4] LI Taochang, Adaptive Sliding Mode Path Tracking Control of Agricultural Wheeled Mobile Robots, China Mechanical Engineering, 29, 5, pp. 579-584, (2018)
[5] GONG J W, XU W, JIANG Y, Et al., Multi-constrained Model Predictive Control for Autonomous Ground Vehicle Trajectory Tracking[J], Journal of Beijing Institute of Technology, 24, 4, pp. 441-443, (2015)
[6] WU Xing, LOU Peihuang, Optimal Path Tracking Control Based on Motion Prediction, Control and Decision, 24, 4, pp. 565-569, (2009)
[7] MACIEJOWSKI J M., Predictive Control with Constraints, (2002)
[8] XI Yugeng, LI Dewei, LIN SHu, Model Predictive Control-Status and Challenges, Acta Automatica Sinica, 39, 3, pp. 222-236, (2013)
[9] GONG Jianwei, JIANG Yan, XU Wei, Model Predictive Control for Self-driving Vehicles, pp. 18-65, (2014)
[10] OYELERE S S., The Application of Model Predictive Control(MPC) to Fast Systems Such as Autonomous Ground Vehicles(AGV)[J], IOSR Journal of Computer Engineering, 3, 3, pp. 27-37, (2014)

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