AAC 2019 Paper Abstract

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Paper MoAT3.4

Selmanaj, Donald (Polytechnic University of Tirana), Corno, Matteo (Politecnico di Milano), Savaresi, Sergio (Politecnico di Milano)

Friction State Classi cation Based on Vehicle Inertial Measurements

Scheduled for presentation during the Regular Session "Control & Estimation I: Vehicle Dynamics" (MoAT3), Monday, June 24, 2019, 12:10−12:30, Chamerolles

9th IFAC International Symposium on Advances in Automotive Control, June 23-27, 2019, Orléans, France

This information is tentative and subject to change. Compiled on March 29, 2024

Keywords Vehicle State Estimation: Sensor Development, Sideslip Angle Observation, Tire and Friction Estimation, Integrated Motion Control: Direct Yaw Control/Electronic Stability Control, 4 Wheel Steering,X-by-Wire, Active Suspensions and Roll Bars

Abstract

Tire-road friction is the most important characteristic defining the planar dynamics of wheeled vehicles. It has consequences on the drivability, stability and tuning of the active vehicle dynamics control systems. This paper proposes two online friction estimation methods designed for the adaptation of vehicle dynamics control algorithms. The problem is framed as a classification problem where inertial measurements are used to discriminate between high and low friction regimes. The first method merges a recursive least-squares (RLS) algorithm with a heuristic bistable logic to classify the friction condition and promptly react to its changes. The second method runs a classification algorithm on the slip-acceleration characteristic. Both methods simultaneously account for the longitudinal and lateral dynamics and are tested on experimental data.

 

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