重庆理工大学学报2026，Vol.40Issue(1)：1-9,9.DOI:10.3969/j.issn.1674-8425(z).2026.01.001
基于K-means++算法划分车辆状态的直接横摆力矩控制
Direct yaw moment control based on K-means++algorithm partitioning of vehicle states
摘要
Abstract
To address the instability of distributed drive electric vehicles under extreme operating conditions(e.g.low adhesion,high-speed steering maneuvers),this paper proposes a hierarchical collaborative control strategy based on K-means++algorithm partitioning of vehicle state regions.First,an offline training dataset is built based on the CarSim vehicle model,extracting nine-dimensional vehicle stability feature parameter(e.g.yaw rate and sideslip angle).The K-means++algorithm is utilized to classify the vehicle state into three regions:stable,coordinated,and control domains,with a dynamic weight coordination module designed accordingly.In the upper controller,the discrete sliding mode control algorithm,integrated with particle swarm optimization for tuning reaching law coefficients,generates target additional yaw moments to track ideal yaw dynamics.Comparative experiments with the integral sliding mode algorithm show the superiority of the discrete sliding mode controller in suppressing peak errors and enhancing tracking accuracy.In the lower controller,a quadratic programming model is built based on stability margin constraints to optimize torque distribution among the four wheels,ensuring that the resultant vectors of longitudinal and lateral forces remain within the friction ellipse.CarSim/Simulink co-simulink demonstrates that,under medium-speed low-adhesion conditions,the proposed strategy reduces peak errors in yaw rate and sideslip angle by 77.2%and 11.6%and improves tracking accuracy by 63.13%and 15.19%compared to the integral sliding mode algorithm.Overall,the results indicate the K-means++state partitioning and hierarchical discrete sliding mode control enhances lateral stability and robustness.关键词
分布式驱动汽车/K-means++算法/车辆状态区域/离散滑模算法Key words
distributed drive vehicle/K-means++algorithm/vehicle state region/discrete sliding mode分类
交通工程引用本文复制引用
潘公宇,李桐..基于K-means++算法划分车辆状态的直接横摆力矩控制[J].重庆理工大学学报,2026,40(1):1-9,9.基金项目
国家自然科学基金项目(52072157) （52072157）
