Integrated steering and braking control for vehicle trajectory tracking under extreme conditions considering road adhesion coefficient—A dynamic weight coefficient allocation strategy

控制理论(社会学) 弹道 跟踪误差 加权 模糊逻辑 计算机科学 车辆动力学 控制器(灌溉) 电子稳定控制 理论(学习稳定性) 混蛋 跟踪(教育) 汽车操纵 模糊控制系统 控制工程 模型预测控制 工程类 补偿(心理学) 控制系统 自适应控制 最优控制 临界制动 方向盘 滑模控制 控制(管理) 主动安全 机制(生物学) 扭矩转向
作者
Mingming Qiu,Qiang Wang,Si-long Liu,Kang Huang,Yong Wang
出处
期刊:Journal of Vibration and Control [SAGE Publishing]
被引量:1
标识
DOI:10.1177/10775463261435475
摘要

Under extreme operating conditions, coordinated steering and braking control is critical for vehicle safety. However, variations in road adhesion coefficients lead to differences in tire force utilization limits, necessitating dynamic adjustment of steering and braking control weights. To address this challenge, this study proposed a trajectory tracking control method with adhesion-aware dynamic weight allocation for steering and braking. Firstly, a lateral-longitudinal coupled vehicle dynamics model is developed, integrating front wheel steering angle and braking deceleration as control inputs. Secondly, a unified MPC framework is designed for trajectory tracking, where fuzzy logic dynamically adjusts the weighting coefficients between control effort and tracking error in the cost function based on real-time vehicle speed and reference trajectory curvature. Furthermore, to account for the influence of road adhesion coefficients on tire force utilization efficiency and driving stability, the mathematical model of weight coefficient correction is established. This two-stage adaptation mechanism integrates fuzzy logic and empirical rules based on mathematical models, enhances steering and braking maneuverability and trajectory tracking accuracy while rigorously guaranteeing stability across the adhesion-variant operating envelope. Simulation results demonstrate that the proposed controller achieves adaptive redistribution of steering and braking authority across different road surfaces, significantly improving handling stability and trajectory tracking precision compared to fixed-weight benchmarks. This method enhances the safety envelope of autonomous vehicles in adhesion-constrained emergency scenarios.
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