Abstract

This paper presents a virtual sensor to estimate the vertical tire forces timely and accurately using stochastic Kalman Filtering and Adaptive Neuro-Fuzzy Inference Systems (ANFIS). Specifically, the suspension dynamics are captured from suspension deflection and vertical acceleration measurements, while the suspension damping, elastic, and kinematic characteristics are approximated by ANFIS models. A virtual testing environment has been constructed in IPG-CarMaker
R using a driver-in-theloop setup and random profiles have been added to the virtual road to approximate the ground irregularities. The virtual sensor has been verified under aggressive maneuvers performed by an experienced driver in a dynamic platform and in a race track (Nordschleife). The virtual sensor is compared to a preliminary quasi-static suspension-based observer developed during previous research steps and results demonstrate the superior ability of the enhanced design to capture wheel dynamic loads derived from road disturbances.
Original languageEnglish
Title of host publicationAccurate Virtual Sensing of Vertical Tire Forces for Enhanced Handling Dynamics
StatePublished - 2017
Event43rd Annual Conference of the IEEE Industrial Electronics Society - Beijing, China

Conference

Conference43rd Annual Conference of the IEEE Industrial Electronics Society
Abbreviated titleIECON 2017
CountryChina
CityBeijing
Period29/10/171/11/17
OtherThe scope of IECON 2017 is in intelligent and computer control systems, robotics, factory communications and automation, flexible manufacturing, data acquisition and signal processing, vision systems, and power electronics.

Fingerprint

sensor
tire
road
deflection
damping
kinematics
disturbance

Cite this

Kanarachos, S., Acosta, M., & Fitzpatrick, M. (2017). Accurate Virtual Sensing of Vertical Tire Forces for Enhanced Handling Dynamics. In Accurate Virtual Sensing of Vertical Tire Forces for Enhanced Handling Dynamics

Accurate Virtual Sensing of Vertical Tire Forces for Enhanced Handling Dynamics. / Kanarachos, Stratis; Acosta, Manuel; Fitzpatrick, Michael.

Accurate Virtual Sensing of Vertical Tire Forces for Enhanced Handling Dynamics. 2017.

Research output: Chapter in Book/Report/Conference proceedingConference proceeding

Kanarachos, S, Acosta, M & Fitzpatrick, M 2017, Accurate Virtual Sensing of Vertical Tire Forces for Enhanced Handling Dynamics. in Accurate Virtual Sensing of Vertical Tire Forces for Enhanced Handling Dynamics. 43rd Annual Conference of the IEEE Industrial Electronics Society, Beijing, China, 29-1 November.
Kanarachos S, Acosta M, Fitzpatrick M. Accurate Virtual Sensing of Vertical Tire Forces for Enhanced Handling Dynamics. In Accurate Virtual Sensing of Vertical Tire Forces for Enhanced Handling Dynamics. 2017.

Kanarachos, Stratis; Acosta, Manuel; Fitzpatrick, Michael / Accurate Virtual Sensing of Vertical Tire Forces for Enhanced Handling Dynamics.

Accurate Virtual Sensing of Vertical Tire Forces for Enhanced Handling Dynamics. 2017.

Research output: Chapter in Book/Report/Conference proceedingConference proceeding

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abstract = "This paper presents a virtual sensor to estimate the vertical tire forces timely and accurately using stochastic Kalman Filtering and Adaptive Neuro-Fuzzy Inference Systems (ANFIS). Specifically, the suspension dynamics are captured from suspension deflection and vertical acceleration measurements, while the suspension damping, elastic, and kinematic characteristics are approximated by ANFIS models. A virtual testing environment has been constructed in IPG-CarMakerR using a driver-in-theloop setup and random profiles have been added to the virtual road to approximate the ground irregularities. The virtual sensor has been verified under aggressive maneuvers performed by an experienced driver in a dynamic platform and in a race track (Nordschleife). The virtual sensor is compared to a preliminary quasi-static suspension-based observer developed during previous research steps and results demonstrate the superior ability of the enhanced design to capture wheel dynamic loads derived from road disturbances.",
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