Vehicle tractive force prediction with robust and windup-stable Kalman filters. (January 2016)

• Record Type: Journal Article
• Authors: Rhode, Stephan; Hong, Sanghyun; Hedrick, J. Karl; Gauterin, Frank
• Is Part Of: Control engineering practice. Volume 46(2016)
• Page Start: 37
• Page End: 50
• Publication Date: 2016-01
• Subjects: Robust -- Poor excitation -- Windup -- Kalman filter -- Vehicle; Lms least mean squares -- KF Kalman filter -- LMS least median squares -- LS least squares -- LTS least trimmed squares -- MKF M-Kalman filter -- MME multiple model estimation -- PKS polynomial Kalman smoother -- RLS recursive least squares -- SGMKF Stenlund–Gustafsson M-Kalman filter; Automatic control -- Periodicals; 629.89
• DOI: 10.1016/j.conengprac.2015.10.002
• Languages: English
• ISSNs: 0967-0661

Abstract

Abstract: Vehicle control systems need to prognosticate future vehicle states in order to improve energy efficiency. This paper compares four approaches that are used to identify the parameters of a longitudinal vehicle dynamics model used for the prediction of vehicle tractive forces. All of the identification approaches build on a standard Kalman filter. Measurement signals are processed using the polynomial function approximation technique to remove noise and compute smooth derivative values of the signals. Experimental results illustrate that the approach using multiple Stenlund–Gustafsson M-Kalman filters (multiple robust and windup-stable Kalman filters) reaches the best performance and robustness in predicting the vehicle tractive forces. Abstract : Highlights: Vehicle tractive forces are predicted with four different type of Kalman filters. A general form of Kalman filter is derived to ensure robustness and windup stability. Multiple Kalman filters based on different model characteristics are fused. Experimental testing compares the performance of the four Kalman filters.