Robust Virtual Sensing of the Vehicle Sideslip Angle through the Cross-Combination of Multiple Filters Using a Decision Tree Algorithm

Autor: Tapus, Gaël P. Atheupe, Younesse El Mrhasli, Ulrich Emabou, Bruno Monsuez, Kenneth Bordignon, Adriana
Jazyk: angličtina
Rok vydání: 2023
Předmět:
Zdroj: Sensors; Volume 23; Issue 13; Pages: 5877
ISSN: 1424-8220
DOI: 10.3390/s23135877
Popis: This paper presents a state-of-the-art estimation technique by cross-combining a number n of filters for high-precision, reliable and robust vehicle sideslip angle state estimation, over a full range of vehicle operations irrespective of the driving mission and disruptions that may occur in the system. A machine-learning algorithm based on decision trees connects several filters together to switch between them according to the driving context, ensuring the best possible state estimate for relatively small and large sideslip angle values. In conjunction with the above-mentioned aspects, a seamless transition between different vehicle models is attained by observing the key parameters characterizing the lateral motion of the vehicle. The tests conducted using a prototype vehicle on a snow-covered track confirm the effectiveness and reliability of the proposed approach.
Databáze: OpenAIRE
Externí odkaz:
Nepřihlášeným uživatelům se plný text nezobrazuje