Alshawi Aymen, De Pinto Stefano, Stano Pietro, van Aalst Sebastiaan, Praet Kylian, Boulay Emilie, Ivone Davide, Gruber Patrick, Sorniotti Aldo
Centre for Automotive Engineering, University of Surrey, Guildford GU2 7XH, UK.
McLaren Automotive, Woking GU21 4YH, UK.
Sensors (Basel). 2024 Jan 10;24(2):436. doi: 10.3390/s24020436.
This paper presents a novel unscented Kalman filter (UKF) implementation with adaptive covariance matrices (ACMs), to accurately estimate the longitudinal and lateral components of vehicle velocity, and thus the sideslip angle, tire slip angles, and tire slip ratios, also in extreme driving conditions, including tyre-road friction variations. The adaptation strategies are implemented on both the process noise and measurement noise covariances. The resulting UKF ACM is compared against a well-tuned baseline UKF with fixed covariances. Experimental test results in high tyre-road friction conditions show the good performance of both filters, with only a very marginal benefit of the ACM version. However, the simulated extreme tests in variable and low-friction conditions highlight the superior performance and robustness provided by the adaptation mechanism.
本文提出了一种具有自适应协方差矩阵（ACM）的新型无迹卡尔曼滤波器（UKF）实现方法，用于精确估计车辆速度的纵向和横向分量，进而估计侧偏角、轮胎滑移角和轮胎滑移率，即使在极端驾驶条件下，包括轮胎与路面摩擦变化时也能如此。自适应策略同时应用于过程噪声和测量噪声协方差。将所得的UKF ACM与具有固定协方差的经过良好调优的基线UKF进行比较。在高轮胎与路面摩擦条件下的实验测试结果表明，两种滤波器性能都很好，ACM版本仅具有非常微小的优势。然而，在可变和低摩擦条件下的模拟极端测试突出了自适应机制所提供的卓越性能和鲁棒性。
