Adaptive vehicle speed control with input injections for longitudinal motion independent road frictional condition estimation

Yan Chen, Junmin Wang

Research output: Contribution to journalArticle

138 Scopus citations

Abstract

This paper presents a novel real-time tire-road friction coefficient estimation method that is independent of vehicle longitudinal motion for ground vehicles with separable control of the front and rear wheels. The tire-road friction coefficient information is of critical importance for vehicle dynamic control systems and intelligent autonomous vehicle applications. In this paper, the vehicle longitudinal-motion-independent tire-road friction coefficient estimation method consists of three main components: 1) an observer to estimate the internal state of a dynamic LuGre tire model; 2) an adaptive control law with a parameter projection mechanism to track the desired vehicle longitudinal motion in the presence of tire-road friction coefficient uncertainties and actively injected braking excitation signals; and 3) a recursive least square estimator that is independent of the control law, to estimate the tire-road friction coefficient in real time. Simulation results based on a high-fidelity CarSim full-vehicle model show that the system can reliably estimate the tire-road friction coefficient independent of vehicle longitudinal motion.

Original languageEnglish (US)
Article number5692858
Pages (from-to)839-848
Number of pages10
JournalIEEE Transactions on Vehicular Technology
Volume60
Issue number3
DOIs
StatePublished - Mar 1 2011
Externally publishedYes

Keywords

  • Adaptive control
  • electric ground vehicle (EGV)
  • in-wheel/hub motor
  • motion-independent persistent excitation
  • tire-road friction coefficient estimation

ASJC Scopus subject areas

  • Automotive Engineering
  • Aerospace Engineering
  • Electrical and Electronic Engineering
  • Applied Mathematics

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