具有数据丢失补偿的轮式后驱无人车辆稳态漂移数据驱动控制方法

doi:10.3901/JME.260585

机械工程学报 ›› 2026, Vol. 62 ›› Issue (12): 360-379.doi: 10.3901/JME.260585

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具有数据丢失补偿的轮式后驱无人车辆稳态漂移数据驱动控制方法

刘翼¹, 范晶晶^2,3, 范丽丽², 孟祥林⁴, 孔德聪⁵, 刘涛⁵, 杜甫^1,2, 刘世达³

1. 北京理工大学机械与车辆学院北京 100081;
2. 江苏智能无人装备产业创新中心有限公司溧阳 213300;
3. 北方工业大学电气与控制工程学院北京 100093;
4. 南京理工大学机械工程学院南京 210094;
5. 西部智车(重庆)科技有限公司重庆 400050

收稿日期:2025-07-27 修回日期:2025-12-19 发布日期:2026-08-03
作者简介:刘翼，男，1988年出生，博士研究生。主要研究方向为车辆动力学控制、智能驾驶控制。E-mail:yliu@niicie.com
刘世达(通信作者)，男，1988年出生，博士，副教授。主要研究方向为无人驾驶数据驱动控制，特种机器人感知与控制。E-mail:lsdshiwo@hotmail.com
基金资助:
国家自然科学基金(52332013)、高机动防暴车辆技术国家工程研究中心开放基金(2023NELEV001)、北京市自然科学基金(L241054)、安徽省科技攻坚计划(202423d12050009)、合肥市科技重大专项“揭榜挂帅”(2022-SZD-008)资助项目。

Data-driven Control Method for Steady-state Drift of Rear-wheel-drive Unmanned Vehicles with Data Loss Compensation

LIU Yi¹, FAN Jingjing^2,3, FAN Lili², MENG Xianglin⁴, KONG Decong⁵, LIU Tao⁵, DU Fu^1,2, LIU Shida³

1. School of Mechanical Engineering, Beijing Institute of Technology, Beijing 100081;
2. Jiangsu Industrial Innovation Center of Intelligent Equipment Co. Ltd, Liyang 213300;
3. School of Electrical and Control Engineering, North China University of Technology, Beijing 100093;
4. School of mechanical engineering, Nanjing University of Science and Technology, Nanjing 210094;
5. Western Intelligent Vehicle Technology Co., Ltd, Chongqing 400050

Received:2025-07-27 Revised:2025-12-19 Published:2026-08-03

摘要/Abstract

摘要： 稳态漂移控制可以发挥出轮式无人车辆的极限运动能力，在特定场景下还能提升行驶安全性能。然而，传统的无人车辆漂移控制通常依赖精确的动力学模型，在建模不准、约束过多及数据丢失等复杂情况下，其效果可能不及预期。为此，提出一种基于动态模糊神经网络的无模型自适应控制方法，以实现数据驱动控制优化。首先，引入一种新型动态线性化技术，将原始的非线性漂移模型等效为动态线性化模型，摆脱对模型精确度的依赖。然后，结合各类约束，设计出伪雅可比矩阵的时变计算方法，并由此获得无模型自适应控制律。接着，引入基于扩展径向基的动态模糊神经网络，充分利用在线和离线数据进行训练，对丢失数据进行有效补偿。伪雅可比矩阵的有界性、控制系统的误差收敛性和稳定性均进行严格的数学证明。仿真试验表明所提数据驱动控制方法的可行性和有效性，实车试验则进一步证明其实用性。

关键词: 无人车辆漂移, 无模型自适应控制, 数据丢失补偿, 伪雅可比矩阵, 动态模糊神经网络

Abstract: Steady-state drift control can utilize the ultimate dynamic capabilities of unmanned wheeled vehicles, and in certain scenarios, it can also enhance driving safety performance. However, traditional control methods, which are typically built upon precise system models, often yield unsatisfactory results when confronted with complex conditions involving model inaccuracies, multiple constraints, and data loss. To address this problem, a Model-free Adaptive Control Algorithm based on Dynamic Fuzzy Neural Network under constraints (DFNN-cMFAC) is proposed to achieve data-driven control optimization. Firstly, a novel dynamic linearization technique is introduced to equivalently transform the original nonlinear drift model into a dynamically linearized model, thereby eliminating dependence on model precision. Subsequently, incorporating various constraints, a time-varying calculation method for the pseudo-Jacobian matrix (PJM) is designed, leading to the derivation of the model-free adaptive control law. Next, an extended radial basis function (ERBF)-based DFNN is integrated to leverage both online and offline data for training, enabling effective compensation for lost data. Rigorous mathematical proofs are provided for the boundedness of the PJM, as well as the error convergence and stability of the control system. Simulation studies demonstrate the feasibility and effectiveness of the proposed data-driven control method, while real-vehicle tests further validate its practicality.

Key words: unmanned vehicle drift, model-free adaptive control, data packet loss compensation, pseudo Jacobian matrix, dynamic fuzzy neural network

中图分类号:

U463
TP273

刘翼, 范晶晶, 范丽丽, 孟祥林, 孔德聪, 刘涛, 杜甫, 刘世达. 具有数据丢失补偿的轮式后驱无人车辆稳态漂移数据驱动控制方法[J]. 机械工程学报, 2026, 62(12): 360-379.

LIU Yi, FAN Jingjing, FAN Lili, MENG Xianglin, KONG Decong, LIU Tao, DU Fu, LIU Shida. Data-driven Control Method for Steady-state Drift of Rear-wheel-drive Unmanned Vehicles with Data Loss Compensation[J]. Journal of Mechanical Engineering, 2026, 62(12): 360-379.

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具有数据丢失补偿的轮式后驱无人车辆稳态漂移数据驱动控制方法

Data-driven Control Method for Steady-state Drift of Rear-wheel-drive Unmanned Vehicles with Data Loss Compensation

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