基于T-S模糊变权重MPC的智能车轨迹跟踪控制

doi:10.3901/JME.2023.04.199

机械工程学报 ›› 2023, Vol. 59 ›› Issue (4): 199-212.doi: 10.3901/JME.2023.04.199

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基于T-S模糊变权重MPC的智能车轨迹跟踪控制

李韶华¹, 杨泽坤^1,2, 王雪玮¹

1. 石家庄铁道大学省部共建交通工程结构力学行为与系统安全国家重点实验室石家庄 050043;
2. 石家庄铁道大学机械工程学院石家庄 050043

收稿日期:2022-06-07 修回日期:2022-11-03 出版日期:2023-02-20 发布日期:2023-04-24
通讯作者: 杨泽坤(通信作者),男,1996年出生。主要研究方向为车辆系统动力学与控制。E-mail:yzk@stdu.edu.cn
作者简介:李韶华,女,1973年出生,博士,教授,博士研究生导师。主要研究方向为车辆系统动力学与控制。E-mail:lishaohua@stdu.edu.cn;王雪玮,女,1993年出生,博士,硕士研究生导师。主要研究方向为车辆系统动力学与控制。E-mail:xwwang@stdu.edu.cn
基金资助:
国家自然科学基金(11972238)和河北省重点研发计划(21342202D)资助项目。

Trajectory Tracking Control of an Intelligent Vehicle Based on T-S Fuzzy Variable Weight MPC

LI Shaohua¹, YANG Zekun^1,2, WANG Xuewei¹

1. State Key Laboratory of Mechanical Behavior and System Safety of Traffic Engineering Structures, Shijiazhuang Tiedao University, Shijiazhuang 050043;
2. School of Mechanical Engineering, Shijiazhuang Tiedao University, Shijiazhuang 050043

Received:2022-06-07 Revised:2022-11-03 Online:2023-02-20 Published:2023-04-24

摘要/Abstract

摘要： 为了协调智能驾驶车辆的轨迹跟踪精确性和稳定性，提高控制算法对不同工况的自适应能力，提出基于Takagi-Sugeno模糊变权重模型预测控制(Takagi-Sugeno fuzzy model predictive control，T-S FMPC)的轨迹跟踪控制策略。以前轮转角为控制变量建立MPC控制，并以实时横向位移误差和横摆角误差为模糊输入，通过T-S模糊控制在线优化MPC目标函数权重，协调权重矩阵对轨迹跟踪精确性和稳定性的影响。基于Carsim建立分布式驱动电动汽车的整车动力学模型，基于Simulink建立控制策略，通过双移线工况仿真及实车试验，验证了所提控制策略的有效性。仿真结果表明，相比于传统MPC控制，所提出的T-S模糊变权重MPC控制可降低横向位移误差达62.24%，有效提高轨迹跟踪精度；并且可使前轮转角波动减小37.46%、横摆角误差减小84.19%，显著增强轨迹跟踪稳定性；试验结果表明，在20 km/h、沥青路面双移线工况下，横向位移误差在0.12 m以内，横摆角误差在1°以内，且前轮转角控制曲线平滑，说明所提算法具有良好的控制效果和实用性。

关键词: 智能驾驶, 轨迹跟踪, 模型预测, T-S模糊, 工况自适应

Abstract: In order to coordinate the trajectory tracking accuracy and stability of intelligent driving vehicles and improve the self-adaptive capability of the control algorithm to different working conditions, a trajectory tracking control strategy based on T-S (Takagi-Sugeno) fuzzy variable weight model predictive control (MPC) is proposed. The MPC control is established with the front wheel steering angle as the control variable. The real-time lateral displacement deviation and yaw angle deviation are taken as the fuzzy inputs. The MPC objective function weights are optimized online by T-S fuzzy control to coordinate the influence of the weight matrix on the trajectory tracking accuracy and stability. A whole vehicle dynamic model of distributed drive electric vehicle is established based on Carsim, the control strategy is established based on Simulink, and the effectiveness of the proposed control strategy is verified through dynamics simulation real vehicle test on double-lane change working condition. Simulation results show that, compared with the traditional MPC control, the proposed T-S fuzzy variable weight MPC control can reduce the lateral displacement deviation by 62.24% and effectively improve the trajectory tracking accuracy. Moreover, it can reduce the front wheel steering angle fluctuation by 37.46% and the yaw angle deviation by 84.19%, which significantly enhances the trajectory tracking stability. The test results show that the lateral displacement deviation is within 0.12 m and the yaw angle deviation is within 1 ° when the vehicle makes double lane change at 20 km/h on asphalt pavement. Meanwhile, the curve of front wheel steering angle is smooth, which indicates that the proposed algorithm has good control effect and practicality.

Key words: intelligent driving, trajectory tracking, model prediction, T-S fuzzy, condition adaption

中图分类号:

U461

李韶华, 杨泽坤, 王雪玮. 基于T-S模糊变权重MPC的智能车轨迹跟踪控制[J]. 机械工程学报, 2023, 59(4): 199-212.

LI Shaohua, YANG Zekun, WANG Xuewei. Trajectory Tracking Control of an Intelligent Vehicle Based on T-S Fuzzy Variable Weight MPC[J]. Journal of Mechanical Engineering, 2023, 59(4): 199-212.

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基于T-S模糊变权重MPC的智能车轨迹跟踪控制

Trajectory Tracking Control of an Intelligent Vehicle Based on T-S Fuzzy Variable Weight MPC

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