Vehicle dynamics estimation using Kalman filter : practical applications

Vehicle Dynamics Estimation using Kalman Filtering Experimental Validation Moustapha Doumiati Ali Charara Alessandro Victorino Daniel Lechner Series Editor Bernard Dubuisson ©WILEY Table of Contents Preface xiii Introduction xvii 1.1. Needs of ADAS systems xvii 1.2. Limitation of available ADAS systems xix 1.3. This book xix 1.4. versus Laboratory existing studies vehicle xx 1.5. Outline xxi Chapter 1. Modeling of Tire and Vehicle 1 Dynamics 1.1. Introduction 1.2. Tire dynamics 1 2 . 1.2.1. Tire forces and moments 2 1.2.1.1. Vertical/normal forces 1.2.1.2. Longitudinal forces and 1.2.1.3. Lateral forces and 1.2.1.4. 1.2.1.5. 2 longitudinal slip ratio 5 Aligning Coupling effects between longitudinal 1.2.2. Tire-road friction coefficient longitudinal 7 traction force 1.2.2.2. Normalized lateral traction force Quasi-static tire model 1.2.3.1. 1.2.3.2. Pacejka's magic tire Dugoff's tire model 1.2.3.3. Linear model 1.2.4. Transient tire model 1.3. Wheel rotational and lateral 6 tire forces 1.2.3. 4 sideslip angle moment 1.2.2.1. Normalized 3 dynamics 9 9 10 model 11 17 18 18 19 viii Vehicle Dynamics Estimation using Kalman Filtering 1.3.1. Static tire radius 20 1.3.2. Effective tire radius 20 1.4. Vehicle 21 body dynamics 1.4.1. Vehicle's vertical 1.4.1.1. Suspension 22 dynamics 23 functions 23 1.4.1.2. Quarter-car vehicle model 1.4.2. Vehicle 25 planar dynamics 25 1.4.2.1. Four-wheel vehicle model 1.4.2.2. Wheel-ground Bicycle model 1.4.2.3. 1.4.3. Roll dynamics 27 vertical forces calculation 30 and lateral load transfer evaluation 34 1.5. Summary Chapter 2. Estimation Methods Based on Kalman Filtering 37 37 2.1. Introduction 2.2. 31 State-space representation and system 38 observability 39 2.2.1. Linear system 39 2.2.2. Nonlinear system 40 2.3. Estimation method: why stochastic models? Closed-loop observer 41 2.3.2. Choice of the observer type 42 2.3.1. 2.4. The linear Kalman filter 43 2.5. Extension to the nonlinear case 44 2.6. The unscented Kalman filter 46 46 2.6.1. Unscented transformation 2.6.2. UKF 48 algorithm 2.7. Illustration of a linear Kalman filter application: road profile 50 estimation 50 2.7.1. Motivation 51 2.7.2. Observer design 2.7.3. Experimental 2.7.3.1. 2.7.3.2. 2.8. results: observer evaluation 53 53 signal Comparison with LPA Comparison with GMP 56 signal 59 Summary Chapter 61 3. Estimation of the Vertical Tire Forces 61 3.1. Introduction 62 3.1.1. Related works 62 3.2. Algorithm description 3.3. Techniques for lateral load scheme transfer calculation in an open-loop 64 Table of Contents ix 3.3.1. Lateral acceleration calculation 65 3.3.2. Roll 65 angle calculation 3.3.3. Limitation of the open-loop 66 model 3.4. Observer design for vertical forces estimation 67 3.5. Vertical forces estimation 69 3.5.1. Observer Ofze design 3.5.2. Observer Opzl 3.6. 3.7. 3.8. 70 formulation the Analysis concerning two-part Models observability analysis Determining the vehicle's mass 3.8.1. Experimental validation of 72 estimation 73 strategy 74 74 the vehicle's weight identification method 75 3.9. Detection of rollover avoidance: LTR evaluation 76 3.10. 78 Experimental validation 3.10.1. Regulation of observers 80 3.10.2. Evaluation of observers 81 3.10.3. Road results experimental Starting-slalom-braking test 82 3.10.3.1. 3.10.3.2. Circle-braking 86 82 test 87 3.10.3.3. Turn test 3.10.3.4. Concluding remarks Comparison between Ofzl versus Ofze 3.10.5. Observability results 3.10.4. 93 linear and nonlinear observers: 93 94 3.10.6. LTR evaluation 94 3.10.7. Road geometry effects 97 3.11. 99 Summary Chapter 4. Estimation of the Lateral Tire Forces 101 101 4.1. Introduction 4.2. Background on lateral force parameters calculation 104 4.2.1.2. Tire-road friction estimation 105 4.2.1.3. 106 Cornering stiffness estimation 4.2.1.4. Effect of camber 106 angle 4.3. Lateral force reconstruction in an open-loop scheme 4.3.1. Test at low lateral acceleration level 4.3.2. Test 4.4. 102 103 4.2.1. Lateral force parameters evaluation 4.2.1.1. Sideslip angle estimation at Techniques 107 108 high lateral acceleration level 112 for lateral tire force evaluation 112 4.5. Estimation process for force estimation sideslip angle and individual lateral tire 115 x Vehicle Dynamics Estimation using Kalman Filtering 4.5.1. Estimation 116 algorithm 4.5.2. Vehicle model 117 4.5.3. 118 Dynamic tire model representation 4.5.4. Reference lateral tire force model 119 4.5.5. Further consideration for the cornering stiffness Ca 120 4.5.6. Lateral force observers: 121 4.5.7. 4.5.8. Estimation 4.5.9. 4.6. Experimental 4.7.2. 124 methodologies Sensitivity analysis 4.7. Pavement 4.7.1. state-space representation Observability analysis of the 124 sideslip angle estimation validation 125 results 128 bend combination test 128 experimental Left-right Single left bend test 132 4.7.3. Slalom test 4.7.4. Circle 4.7.5. 137 141 test Longitudinal forces estimation 4.7.6. Concluding remarks 4.7.7. 4.7.8. 4.8. Ofve versus Tuning Analysis on 143 experimental results Ofvu 153 and observations 154 respect to road friction variation Summary Chapter 156 158 5. Embedded Real-Time Experimental 152 153 of observers 4.8.1. Robustness with 4.9. 125 System Results for Vehicle State Estimation: 159 5.1. Introduction 159 5.2. 159 Laboratory vehicle 5.2.1. Embedded sensors 160 5.2.2. Software modules 164 5.2.3. DLL configuration 164 5.3. Estimation process: VSO system 165 5.4. Test tracks 167 5.5. Test results 168 5.5.1. Bourbriac test 169 5.5.2. Callac 179 test 5.5.3. Rostrenen 5.5.4. 5.6. test Concluding Summary remarks 185 199 200 Appendices 201 Appendix 203 1 Table of Contents xi Appendix 2 207 Appendix 3 209 Appendix 4 Appendix 5 221 Appendix 6 225 Bibliography 227 Index 237 217