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融合前馈及状态反馈的智能汽车模型预测控制

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  • 国家科技期刊平台
  • NETL
  • NSTL
  • 万方数据
针对具有动力学约束的智能汽车路径精确跟踪问题,提出了一种融合前馈及状态反馈的模型预测控制(model predictive control,MPC)方法.首先,根据车辆二自由度模型建立MPC路径跟踪基础模型,然后考虑基础模型中道路曲率变化对系统产生的已建模稳态扰动,设计前馈控制器(feed-forward control,FFC)进行消除;并进一步采用比例积分微分(propor-tional integral derivative,PID)控制器进行系统误差状态反馈调节;最终形成融合前馈及状态反馈转角输入的模型预测最优调节控制律(MPC-FF-PID).最后基于MATLAB/Simulink和Carsim平台证实所提算法的有效性,并基于智能驾驶实车平台在园区低速场景下进行实车测试,最大横向和航向误差分别为0.128 7 m和0.063 9 rad,表明本文算法具备更高的跟踪精度及安全性.
Model Predictive Control for Intelligent Vehicles Fusing Feed-forward and State Feedback
In this paper,a model predictive control(MPC)method integrating feed-forward and state feedback is proposed for the problem of accurate path tracking for intelligent vehicles with dynamic constraints.Firstly,the MPC path tracking base model is established according to the vehicle two-degree-of-freedom model,and then,the modeled steady-state perturbations generated by the road curvature changes on the system in the base model are considered and designed to be eliminated by feed-forward control(FFC);Furthermore,the proportional integral derivative(PID)controller is used to regulate the system error state feedback;Meanwhile,the model predictive optimal regulation control law(MPC-FF-PID)is verified by integrating the feed-forward and state feedback corner inputs.Finally,the effectiveness of the proposed algorithm is confirmed based on MATLAB/Simulink and Carsim platforms,and a real vehicle test is carried out in the low-speed scenario in the park based on the intelligent driving real vehicle platform,and the maximum lateral and heading errors are 0.128 7 m and 0.063 9 rad,respectively,indicating that the proposed algorithm has higher tracking accuracy and safety.

intelligent vehiclespath trackingmodel predictive controlfeed-forward controlPID control based on state feedbacksimulation and real vehicle verification

陈齐平、曹天恒、黄少堂、江会华、江志强、时乐泉

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华东交通大学 载运工具与装备教育部重点实验室,江西 南昌 330013

江铃汽车股份有限公司,江西 南昌 330001

江西交通职业技术学院 汽车工程学院,江西 南昌 330013

智能汽车 路径跟踪 模型预测控制 前馈控制 状态反馈PID控制 仿真与实车验证

江西省重点研发计划重点项目江西省重点研发计划重点项目江西省03专项及5G项目

20212BBE5101420224BBE5200320232ABC03A30

2024

10.16339/j.cnki.hdxbzkb.2024187

湖南大学学报(自然科学版)
湖南大学

湖南大学学报(自然科学版)

CSTPCD北大核心
影响因子：0.651
ISSN：1674-2974
年,卷(期)：2024.51(8)
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