| 张庭芳,凌勇,谢世坤,吴晓建,刘建胜.自适应扩展卡尔曼滤波在车辆状态估计中的优化研究[J].井冈山大学自然版,2025,46(2):89-96 |
| 自适应扩展卡尔曼滤波在车辆状态估计中的优化研究 |
| OPTIMIZATION OF ADAPTIVE EXTENDED KALMAN FILTER IN VEHICLE STATE ESTIMATION |
| 投稿时间：2024-11-26 修订日期：2024-12-30 |
| DOI：10.3969/j.issn.1674-8085.2025.02.011 |
| 中文关键词: 车辆状态估计 EKF算法 自适应控制 车辆动力学模型 H.B.Pacejke轮胎模型 |
| 英文关键词: vehicle state estimation EKF algorithm adaptive control vehicle dynamics model H.B.Pacejke tire model |
| 基金项目:国家自然科学基金项目(52262054) |
| 作者 | 单位 | E-mail | | 张庭芳 | 南昌大学先进制造学院, 江西, 南昌 330031 | | | 凌勇 | 南昌大学先进制造学院, 江西, 南昌 330031 | | | 谢世坤 | 井冈山大学机电工程学院, 江西, 吉安 343009 | xskun@163.com | | 吴晓建 | 南昌大学先进制造学院, 江西, 南昌 330031 | | | 刘建胜 | 南昌大学先进制造学院, 江西, 南昌 330031 | liujiansheng@ncu.edu.cn |
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| 中文摘要: |
| 针对传统扩展卡尔曼滤波（EKF）算法中的系统过程噪声和观测噪声，都需要依靠人工经验进行参数设定这一问题，提出一种改进的自适应扩展卡尔曼滤波（AEKF）算法，用于车辆状态估计。首先，本实验采用车辆非线性三自由度动力学模型和 H.B.Pacejke 轮胎模型作为估计模型。而后将传统的系统过程噪声和观测噪声协方差矩阵设计成随前轮转角和车速变化而变化的自适应矩阵，减少外部随机噪声的干扰。最后采用 Carsim 与Matlab/Simulink 仿真软件对所提算法进行不同工况的仿真验证，并与传统的 EKF 算法进行比较。仿真实验结果显示：与传统的 EKF 算法相比，所改进的 AEKF 算法不仅能适应不同工况且不受外部噪声干扰影响，还能提高一定的估计精度。 |
| 英文摘要: |
| In order to solve the problem that the system process noise and observation noise in the traditional extended Kalman filter (EKF) algorithm rely on artificial experience for parameter setting, an improved adaptive extended Kalman filter (AEKF) algorithm was proposed to estimate the vehicle state. Firstly, the nonlinear three-degree-of-freedom dynamics model and the H.B. Pacejke tire model were used as the estimation models. Then, the traditional system process noise and measurement noise covariance matrix were designed into an adaptive matrix that changed with the change of front wheel angle and vehicle speed to reduce the interference of external random noise. Finally, Carsim and Matlab/Simulink simulation software were used to simulate and verify the proposed algorithm under different working conditions, and compare it with the traditional EKF algorithm. The results show that compared with the traditional EKF algorithm, the improved AEKF algorithm can not only adapt to different working conditions and not be affected by external noise interference, but also improve the estimation accuracy. |
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