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Vehicle Dynamics Estimation using Kalman Filtering: Experimental Validation

ISBN: 978-1-84821-366-1
304 pages
December 2012, Wiley-ISTE
Vehicle Dynamics Estimation using Kalman Filtering: Experimental Validation (1848213662) cover image

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. Improving vehicle control and stabilization is possible when vehicle dynamic variables are known. The fundamental problem is that some essential variables related to tire/road friction are difficult to measure because of technical and economical reasons. Therefore, these data must be estimated.
It is against this background, that this book’s objective is to develop estimators in order to estimate the vehicle’s load transfer, the sideslip angle, and the vertical and lateral tire/road forces using a roll model. The proposed estimation processes are based on the state observer (Kalman filtering) theory and the dynamic response of a vehicle instrumented with standard sensors. These estimators are able to work in real time in normal and critical driving situations. Performances are tested using an experimental car in real driving situations. This is exactly the focus of this book, providing students, technicians and engineers from the automobile field with a theoretical basis and some practical algorithms useful for estimating vehicle dynamics in real-time during vehicle motion.

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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

1.2.2.1. Normalized longitudinal traction force

1.2.2.2. 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

viii Vehicle Dynamics Estimation using Kalman Filtering

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

1.4.2.3. Bicycle model

1.4.3. 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

2.7. Illustration of a linear Kalman filter application: road profile estimation

2.7.1. 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

3.4. Observer design for vertical forces estimation

3.5. 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. Observers regulation

3.10.2. Observers evaluation

3.10.3. Road experimental results

3.10.3.1. Starting-slalom-braking test

3.10.3.2. Circle-braking test

3.10.3.3. Turn test

3.10.3.4. Concluding remarks

3.10.4. Comparison between linear and nonlinear observers: OFz L versus OFzE

3.10.5. Observability results

3.10.6. LTR evaluation .

3.10.7. Road geometry effects

3.11. Summary

Chapter 4. Estimation of the Lateral Tire Forces

4.1. Introduction

4.2. Background on lateral force parameters calculation

4.2.1. Lateral force parameters evaluation

4.2.1.1. Sideslip angle estimation

4.2.1.2. Tire–road friction estimation

4.2.1.3. Cornering stiffness estimation

4.2.1.4. Effect of camber angle

4.3. Lateral force reconstruction in an open-loop scheme

4.3.1. Test at low lateral acceleration level

4.3.2. Test at high lateral acceleration level

4.4. Techniques for lateral tire force evaluation

4.5. Estimation process for sideslip angle and individual lateral tire force estimation

x Vehicle Dynamics Estimation using Kalman Filtering

4.5.1. Estimation algorithm

4.5.2. Vehicle model

4.5.3. Dynamic tire model representation

4.5.4. Reference lateral tire force model

4.5.5. Further consideration for the cornering stiffness Cα

4.5.6. Lateral force observers: state-space representation

4.5.7. Observability analysis

4.5.8. Estimation methodologies

4.5.9. Sensitivity analysis of the sideslip angle estimation

4.6. Experimental validation

4.7. Pavement experimental results

4.7.1. Left–right bend combination test

4.7.2. Single left bend test

4.7.3. Slalom test

4.7.4. Circle test

4.7.5. Longitudinal forces estimation

4.7.6. Concluding remarks on experimental results

4.7.7. OFyE versus OFy U

4.7.8. Observers tuning

4.8. Analysis and observations

4.8.1. Robustness with respect to road friction variation

4.9. Summary

Chapter 5. Embedded Real-Time System for Vehicle State Estimation: Experimental Results

5.1. Introduction

5.2. Laboratory vehicle

5.2.1. Embedded sensors

5.2.2. Software modules

5.2.3. DLL configuration

5.3. Estimation process: VSO system

5.4. Test tracks

5.5. Test results

5.5.1. Bourbriac test

5.5.2. Callac test

5.5.3. Rostrenen test

5.5.4. Concluding remarks

5.6. Summary

APPENDICES

Bibliography

Index

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