rdf-syntax-grammar
mods:
  schema location: http://www.loc.gov/mods/v3 http://www.loc.gov/standards/mods/v3/mods-3-2.xsd
  title info:
    title: Vehicle dynamics estimation using Kalman filtering
    sub title: experimental validation
  name:
    name part: Doumiati, Moustapha
    name part: 1983-
  type of resource: text
  genre: bibliography
  genre: Electronic books.
  origin info:
    place:
      place term: enk
    date issued: 2013
    issuance: monographic
  language:
    language term: eng
  physical description:
    form: remote
    extent: 1 online resource (xxii, 239 pages) : illustrations.
  abstract: Vehicle dynamics and stability have been of considerable interest for a number of years. The obvious dilemma is that people naturally desire to drive faster and faster yet expect their vehicles to be "infinitely" stable and safe during all normal and emergency maneuvers. For the most part, people pay little attention to the limited handling potential of their vehicles until some unusual behavior is observed that often results in accidents and even fatalities. This book presents several model-based estimation methods which involve information from current potential-integrable sensors. I.
  table of contents: Title Page; Contents; Preface; Introduction; I.1. Needs of ADAS systems; I.2. Limitation of available ADAS systems; I.3. This book versus existing studies; I.4. Laboratory vehicle; I.5. Outline; Chapter 1. Modeling of Tire and Vehicle Dynamics; 1.1. Introduction; 1.2. Tire dynamics; 1.2.1. Tire forces and moments; 1.2.1.1. Vertical/normal forces; 1.2.1.2. Longitudinal forces and longitudinal slip ratio; 1.2.1.3. Lateral forces and sideslip angle; 1.2.1.4. Aligning moment; 1.2.1.5. Coupling effects between longitudinal and lateral tire forces; 1.2.2. Tire-road friction coefficient.
  table of contents: 1.2.2.1. Normalized longitudinal traction force1.2.2.2. Normalized lateral traction force; 1.2.3. Quasi-static tire model; 1.2.3.1. Pacejka's magic tire model; 1.2.3.2. Dugoff's tire model; 1.2.3.3. Linear model; 1.2.4. Transient tire model; 1.3. Wheel rotational dynamics; 1.3.1. Static tire radius; 1.3.2. Effective tire radius; 1.4. Vehicle body dynamics; 1.4.1. Vehicle's vertical dynamics; 1.4.1.1. Suspension functions; 1.4.1.2. Quarter-car vehicle model; 1.4.2. Vehicle planar dynamics; 1.4.2.1. Four-wheel vehicle model; 1.4.2.2. Wheel-ground vertical forces calculation.
  table of contents: 1.4.2.3. Bicycle model1.4.3. Roll dynamics and lateral load transfer evaluation; 1.5. Summary; Chapter 2. Estimation Methods Based on Kalman Filtering; 2.1. Introduction; 2.2. State-space representation and system observability; 2.2.1. Linear system; 2.2.2. Nonlinear system; 2.3. Estimation method: why stochastic models?; 2.3.1. Closed-loop observer; 2.3.2. Choice of the observer type; 2.4. The linear Kalman filter; 2.5. Extension to the nonlinear case; 2.6. The unscented Kalman filter; 2.6.1. Unscented transformation; 2.6.2. UKF algorithm.
  table of contents: 2.7. Illustration of a linear Kalman filter application: road profile estimation2.7.1. Motivation; 2.7.2. Observer design; 2.7.3. Experimental results: observer evaluation; 2.7.3.1. Comparison with LPA signal; 2.7.3.2. Comparison with GMP signal; 2.8. Summary; Chapter 3. Estimation of the Vertical Tire Forces; 3.1. Introduction; 3.1.1. Related works; 3.2. Algorithm description; 3.3. Techniques for lateral load transfer calculation in an open-loop scheme; 3.3.1. Lateral acceleration calculation; 3.3.2. Roll angle calculation; 3.3.3. Limitation of the open-loop model.
  table of contents: 3.4. Observer design for vertical forces estimation3.5. Vertical forces estimation; 3.5.1. Observer OFzE design; 3.5.2. Observer OFzL formulation; 3.6. Analysis concerning the two-part estimation strategy; 3.7. Models observability analysis; 3.8. Determining the vehicle's mass; 3.8.1. Experimental validation of the vehicle's weight identification method; 3.9. Detection of rollover avoidance: LTR evaluation; 3.10. Experimental validation; 3.10.1. Regulation of observers; 3.10.2. Evaluation of observers; 3.10.3. Road experimental results; 3.10.3.1. Starting-slalom-braking test.
  note: Moustapha Doumiati [and others].
  note: Includes bibliographical references (pages 227-235) and index.
  subject:
    topic: Motor vehicles
    topic: Dynamics
  subject:
    topic: Motor vehicles
    topic: Stability
  subject:
    topic: Kalman filtering
  subject:
    topic: TRANSPORTATION
    topic: Automotive
    topic: Customizing
  subject:
    topic: Kalman filtering
  subject:
    topic: Motor vehicles
    topic: Dynamics
  subject:
    topic: Motor vehicles
    topic: Stability
  classification: TL243 .D68 2013
  classification: 629.2/31
  related item:
    title info:
      title: Vehicle dynamics estimation using Kalman filtering
    origin info:
      publisher: London : Iste ; Hoboken, NJ : Wiley, 2013
  related item:
    title info:
      title: Automation-control and industrial engineering series
  identifier: 9781118579008
  identifier: 1118579003
  identifier: 9781118578988
  identifier: 1118578988
  identifier: 9781118578995
  identifier: 1118578996
  identifier: 9781848213661
  identifier: 1848213662
  identifier: https://doi.org/10.1002/9781118578988
  record info:
    record content source: YDXCP
    record creation date: 130124
    record change date: 20170124070512.9
    record identifier: ocn892431495
    language of cataloging:
      language term: eng