不确定路面附着系数条件下一种基于双层非线性模型预测控制的自动驾驶卡车轨迹规划方法

王鸿超; 张伟伟; 吴训成; 曹昊天; 高巧明; 罗素云

doi:10.1631/FITEE.1900185

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不确定路面附着系数条件下一种基于双层非线性模型预测控制的自动驾驶卡车轨迹规划方法

常规文章 | Updated：2022-05-19

- 不确定路面附着系数条件下一种基于双层非线性模型预测控制的自动驾驶卡车轨迹规划方法
- A double-layered nonlinear model predictive control based control algorithm for local trajectory planning for automated trucks under uncertain road adhesion coefficient conditions
- 信息与电子工程前沿（英文） 2020年21卷第7期页码：1059-1073
- Affiliations：
  
  College of Mechanical and Automotive Engineering, Shanghai University of Engineering Science, Shanghai 201620, China
  State Key Laboratory of Advanced Design and Manufacturing for Vehicle Body, Hunan University, Changsha 410082, China
  College of Automobile and Transportation, Guangxi University of Science and Technology, Liuzhou 545006, China
- Author bio：
  
  [ "Hong-chao WANG, E-mail: 17721336541@163.com" ]
  Wei-wei ZHANG, E-mail:zwwsues@163.com
  [ "Xun-cheng WU, E-mail:longxd2714@163.com" ]
  [ "Hao-tian CAO, E-mail:yjs_liqing@163.com" ]
  [ "Qiao-ming GAO, E-mail:mosxsues@163.com" ]
  [ "Su-yun LUO, E-mail:ly18362885604@163.com" ]
- Funds：
  
  Project supported by the National Fund for Fundamental Research, China (No. 282017Y-5303), the National Natural Science Foundation of China (Nos. 51805312 and 51675324), the Shanghai Sailing Program, China (No. 18YF1409400), the Training and Funding Program of Shanghai College Young Teachers, China (No. ZZGCD15102), the Scientific Research Project of Shanghai University of Engineering Science, China (No. 2016-19), the Shanghai University of Engineering Science Innovation Fund for Graduate Students, China (No. 18KY0610), and the Technology and Innovation Projects of Guangxi Province, China (No. 2017-393)
- DOI：10.1631/FITEE.1900185
  中图分类号： TP273
- 收稿：2019-04-08，
  
  修回：2020-;6-;3，
  
  纸质出版：2020-07
- Accepted：
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王鸿超, 张伟伟, 吴训成, 等. 不确定路面附着系数条件下一种基于双层非线性模型预测控制的自动驾驶卡车轨迹规划方法[J]. 信息与电子工程前沿（英文）, 2020,21(7):1059-1073.

Hong-chao WANG, Wei-wei ZHANG, Xun-cheng WU, et al. A double-layered nonlinear model predictive control based control algorithm for local trajectory planning for automated trucks under uncertain road adhesion coefficient conditions[J]. Frontiers of Information Technology & Electronic Engineering, 2020, 21(7): 1059-1073.
王鸿超, 张伟伟, 吴训成, 等. 不确定路面附着系数条件下一种基于双层非线性模型预测控制的自动驾驶卡车轨迹规划方法[J]. 信息与电子工程前沿（英文）, 2020,21(7):1059-1073. DOI： 10.1631/FITEE.1900185.

Hong-chao WANG, Wei-wei ZHANG, Xun-cheng WU, et al. A double-layered nonlinear model predictive control based control algorithm for local trajectory planning for automated trucks under uncertain road adhesion coefficient conditions[J]. Frontiers of Information Technology & Electronic Engineering, 2020, 21(7): 1059-1073. DOI： 10.1631/FITEE.1900185.

摘要

提出一种双层控制算法以规划配备四轮轮毂电机的自动驾驶卡车的行驶轨迹。该控制算法主要由主层非线性模型预测控制（MLN-MPC）算法和次层非线性模型预测控制（SLN-MPC）算法组成，其中，MLN-MPC控制算法用于规划合理的卡车行驶轨迹，SLN-MPC控制算法将车轮纵向滑移率限制在稳定区域，避免卡车在驱动过程中发生过度打滑。总体而言，该控制算法为一个闭环控制系统。在离线仿真环境下，通过AMESim、Simulink、dSPACE和TruckSim仿真软件联合仿真。仿真结果表明，本文所提算法能规划一条合理的车辆避障行驶轨迹，在不确定路面附着系数条件下能将车辆纵向滑移率控制在合理范围。此外，为评估该算法在实际应用中的可行性，在联合仿真系统中加入驾驶员模型验证该算法的稳定性与鲁棒性。与传统的基于PID控制算法相比，该算法具有更低的计算能耗。

Abstract

We present a double-layered control algorithm to plan the local trajectory for automated trucks equipped with four hub motors. The main layer of the proposed control algorithm consists of a main layer nonlinear model predictive control (MLN-MPC) controller and a secondary layer nonlinear MPC (SLN-MPC) controller. The MLN-MPC controller is applied to plan a dynamically feasible trajectory

and the SLN-MPC controller is designed to limit the longitudinal slip of wheels within a stable zone to avoid the tire excessively slipping during traction. Overall

this is a closed-loop control system. Under the off-line co-simulation environments of AMESim

Simulink

dSPACE

and TruckSim

a dynamically feasible trajectory with collision avoidance operation can be generated using the proposed method

and the longitudinal wheel slip can be constrained within a stable zone so that the driving safety of the truck can be ensured under uncertain road surface conditions. In addition

the stability and robustness of the method are verified by adding a driver model to evaluate the application in the real world. Furthermore

simulation results show that there is lower computational cost compared with the conventional PID-based control method.

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references

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