Traction efficiency of tractors barely reaches 50 % in field operations. On the other hand, modern trends in agriculture show growth of the global tractor markets and at the same time increased demands for greenhouse gas emission reduction as well as energy efficiency due to increasing fuel costs. Engine power of farm tractors is growing at 1.8 kW per year reaching today about 500 kW for the highest traction class machines.
The problem of effective use of energy has become crucial. Existing slip control approaches for tractors do not fulfil this requirement due to fixed reference set-point. The present work suggests an optimal control scheme based on set-point optimization and on assessment of soil conditions, namely, wheel-ground parameter identification using fuzzy-logic-assisted adaptive unscented Kalman filter.:List of figures VIII
List of tables IX
Keywords XI
List of abbreviations XII
List of mathematical symbols XIII
Indices XV
1 Introduction 1
1.1 Problem description and challenges 1
1.1.1 Development of agricultural industry 1
1.1.2 Power flows and energy efficiency of a farm tractor 2
1.2 Motivation 9
1.3 Purpose and approach 12
1.3.1 Purpose and goals 12
1.3.2 Brief description of methodology 14
1.3.2.1 Drive torque feedback 14
1.3.2.2 Measurement signals 15
1.3.2.3 Identification of traction parameters 15
1.3.2.4 Definition of optimal slip 15
1.4 Outline 16
2 State of the art in traction management and parameter estimation 17
2.1 Slip control for farm tractors 17
2.2 Acquisition of drive torque feedback 23
2.3 Tire-ground parameter estimation 25
2.3.1 Kalman filter 25
2.3.2 Extended Kalman filter 27
2.3.3 Unscented Kalman filter 27
2.3.4 Adaptation algorithms for Kalman filter 29
3 Modelling vehicle dynamics for traction control 31
3.1 Tire-soil interaction 31
3.1.1 Forces in wheel-ground contact 32
3.1.1.1 Vertical force 32
3.1.1.2 Tire-ground surface geometry 34
3.1.2 Longitudinal force 36
3.1.3 Zero-slip condition 37
3.1.3.1 Soil shear stress 38
3.1.3.2 Rolling resistance 39
3.2 Vehicle body and wheels 40
3.2.1 Short description of Multi-Body-Simulation 40
3.2.2 Vehicle body and wheel models 42
3.2.3 Wheel structure 43
3.3 Stochastic input signals 45
3.3.1 Influence of trend and low-frequency components 47
3.3.2 Modelling stochastic signals 49
3.4 Further components and general view of tractor model 53
3.4.1 Generator, intermediate circuit, electrical motors and braking resistor 53
3.4.2 Diesel engine 55
4 Identification of traction parameters 56
4.1 Description of identification approaches 56
4.2 Vehicle model 58
4.2.1 Vehicle longitudinal dynamics 58
4.2.2 Wheel rotational dynamics 59
4.2.3 Tire dynamic rolling radius and inner rolling resistance coefficient 60
4.2.4 Whole model 61
4.3 Static methods of parameter identification 63
4.4 Adaptation mechanism of the unscented Kalman filter 63
4.5 Fuzzy supervisor for the adaptive unscented Kalman filter 66
4.5.1 Structure of the fuzzy supervisor 67
4.5.2 Stability analysis of the adaptive unscented Kalman filter with the
fuzzy supervisor 69
5 Optimal slip control 73
5.1 Approaches for slip control by means of traction control system 73
5.1.1 Feedback compensation law 73
5.1.2 Sliding mode control 74
5.1.3 Funnel control 77
5.1.4 Lyapunov-Candidate-Function-based control, other approaches and
choice of algorithm 78
5.2 General description of optimal slip control algorithm 79
5.3 Estimation of traction force characteristic curves 82
5.4 Optimal slip set-point computation 85
6 Verification of identification and optimal slip control systems 91
6.1 Simulation results 91
6.1.1 Identification of traction parameters 91
6.1.1.1 Comparison of extended Kalman filter and unscented Kalman
filter 92
6.1.1.2 Comparison of ordinary and adaptive unscented Kalman filters 96
6.1.1.3 Comparison of the adaptive unscented Kalman filter with the
fuzzy supervisor and static methods 99
6.1.1.4 Description of soil conditions 100
6.1.1.5 Identification of traction parameters under changing soil conditions 101
6.1.2 Approximation of characteristic curves 102
6.1.3 Slip control with reference of 10% 103
6.1.4 Comparison of operating with fixed and optimal slip reference 104
6.2 Experimental verification 108
6.2.1 Setup and description of the experiments 108
6.2.2 Virtual slip control without load machine 109
6.2.3 Virtual slip control with load machine 113
7 Summary, conclusions and future challenges 122
7.1 Summary of results and discussion 122
7.2 Contributions of the dissertation 123
7.3 Future challenges 123
Bibliography 125
A Measurement systems 137
A.1 Measurement of vehicle velocity 137
A.2 Measurement of wheel speed 138
A.3 Measurement or estimation of wheel vertical load 139
A.4 Measurement of draft force 140
A.5 Further possible measurement systems 141
B Basic probability theoretical notions 142
B.1 Brief description of the theory of stochastic processes 142
B.2 Properties of stochastic signals 144
B.3 Bayesian filtering 145
C Modelling stochastic draft force and field microprofile 147
D Approximation of kappa-curves 152
E Simulation parameters 156
| Identifer | oai:union.ndltd.org:DRESDEN/oai:qucosa:de:qucosa:28368 |
| Date | 23 October 2014 |
| Creators | Osinenko, Pavel |
| Contributors | Herlitzius, Thomas, Röbenack, Klaus, Technische Universität Dresden |
| Source Sets | Hochschulschriftenserver (HSSS) der SLUB Dresden |
| Language | English |
| Detected Language | English |
| Type | doc-type:doctoralThesis, info:eu-repo/semantics/doctoralThesis, doc-type:Text |
| Rights | info:eu-repo/semantics/openAccess |
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