Robust Estimation of Vehicle Motion States Utilizing an Extended Set-Membership Filter

• Main Authors: Jianfeng Chen, Congcong Guo, Shulin Hu, Jiantian Sun, Reza Langari, Chuanye Tang
• Format: Article
• Language: English
• Published: MDPI AG 2020-02-01
• Series: Applied Sciences
• Subjects: robust estimation; dynamic model; unknown but bounded noise; extended set-membership filter
• Online Access: https://www.mdpi.com/2076-3417/10/4/1343

Reliable vehicle motion states are critical for the precise control performed by vehicle active safety systems. This paper investigates a robust estimation strategy for vehicle motion states by feat of the application of the extended set-membership filter (ESMF). In this strategy, a system noise source is only limited as unknown but bounded, rather than the Gaussian white noise claimed in the stochastic filtering algorithms, such as the unscented Kalman filter (UKF). Moreover, as one part of this strategy, a calculation scheme with simple structure is proposed to acquire the longitudinal and lateral tire forces with acceptable accuracy. Numerical tests are carried out to verify the performance of the proposed strategy. The results indicate that as compared with the UKF-based one, it not only has higher accuracy, but also can provide a 100% hard boundary which contains the real values of the vehicle states, including the vehicle’s longitudinal velocity, lateral velocity, and sideslip angle. Therefore, the ESMF-based strategy can proffer a more guaranteed estimation with robustness for practical vehicle active safety control.