重庆理工大学学报2025，Vol.39Issue(13)：19-27,9.DOI:10.3969/j.issn.1674-8425(z).2025.07.003
基于RLS-RBPF算法的车辆悬架参数辨识方法研究
Research on identification method of vehicle suspension parameters based on RLS-RBPF algorithm
摘要
Abstract
The condition of the suspension system will inevitably change witha vehicle's operation.To accurately assess the long-term changes in suspension parameters,especially the early fault warning,this paper proposes a suspension parameter identification method based on the actual driving state of the vehicle.First,it installs vibration sensors at key parts of the vehicle to capture vibration acceleration signals.Then,it uses a recursive least squares algorithm to preliminarily identify the spring stiffness and shock absorber damping coefficient of the suspension.On this basis,the Rao-Blackwellized particle filter algorithm is employed to finely optimize the two parameters.Finally,combining the measured vehicle hard point coordinates and the identified suspension parameters,a vehicle dynamics model is constructed based on the principles of multi-body dynamics,and compared with the actual design parameters to verify the accuracy of the identified parameters.Results show the method achieves high accuracy in identifying the spring stiffness and shock absorber damping coefficient of the suspension,with the maximum deviation from the true value being only 2.50％and 1.82％respectively.Meanwhile,the root mean square error of the simulation output of the vehicle dynamics model and the actual measured load spectrum is controlled within 5％.The method markedly improves the accuracy of suspension system parameter identification.关键词
递推最小二乘算法/RBPF算法/实车载荷谱/参数辨识Key words
recursive least squares algorithm/RBPF algorithm/real vehicle load spectrum/parameter identification分类
交通工程引用本文复制引用
王姝,董传昊,张大伟,赵轩,周辰雨,邵帅..基于RLS-RBPF算法的车辆悬架参数辨识方法研究[J].重庆理工大学学报,2025,39(13):19-27,9.基金项目
国家自然科学基金面上项目(52172362,52372375) （52172362,52372375）
陕西省重点研发计划项目(2024GX-YBXM-260) （2024GX-YBXM-260）
陕西省科技成果转化计划项目(2024CG-CGZH-19) （2024CG-CGZH-19）
陕西省自然科学基础研究项目(2022JQ543543) （2022JQ543543）
