Date of Award

2008

Degree Type

Thesis

Department

Mechanical Engineering

First Advisor

Lin, Paul

Subject Headings

Pavements -- Skid resistance, Motor vehicles -- Skidding, Roads -- Design and construction, Vehicle dynamics, Intelligent systems, Detection and identification, Anti-skid control, Extended state observer, Active disturbance rejection control

Abstract

Road surface condition is greatly dependent on the surface's friction coefficient. The abrupt change of the coefficient results in variation of wheel slip which likely leads to vehicle instability. Vehicle steering model and the dynamic equations for four-wheel drive vehicle is developed. A new observer, called Extended State Observer (ESO) is used to estimate the longitudinal velocity, lateral velocity and yaw rate, and more importantly an additional quantity known as system dynamics. A trained neural network was employed to help determine the friction coefficient. Fuzzy logic was employed to quickly detect the change of road surface condition and further classify the surface condition. The presented methods were simulated with a vehicle encountering a significant change from a uniform-Îơ (i.e. uniform friction coefficient) surface to a split-Îơ surface (i.e. different friction coefficient on each side of the wheels) during cornering. The results this obtained show that the developed techniques could effectively detect and identify the road surface condition. Further more, a new anti-skid controller by means of Active Disturbance Rejection Controller (ADRC) and the ESO is proposed. The simulation results show that the controller can effectively control the vehicle's yaw rate while cornering

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