Optimal slip control for tractors with feedback of drive torque

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 15

Adaptive and reconfigurable data fusion architectures in positioning navigation systems

Dans les systèmes de positionnement de véhicules, à tout moment, n'importe lequel des détecteurs peut, temporairement ou de manière permanente, tomber en panne ou cesser d'envoyer des informations. Il s'ensuit alors des répercussions sur la sécurité, la santé, ainsi que des informations financières ou même légales. Bien que les nouvelles pratiques de conception aient tendance à réduire au minimum les défaillances des détecteurs, il est reconnu que de tels évènements peuvent quand même souvenir. Dans un tel cas, le détecteur défectueux doit être identifié et isolé afin d'éviter de corrompre les évaluations globales et, finalement, le système doit être capable de se reconfigurer afin de surmonter le carence causée par la défaillance. En bref, un système de navigation doit être robuste et adaptatif. Cette thèse propose plusieurs architectures de fusion de données capables de s'adapter suite à des défaillances de détecteurs. Les diverses approches utilisent un filtre Kalman en combinaison avec la détection de défauts pour produire des modules de positionnement robuste. Les modules devront être capables de fonctionner dans des situations telles que l'entrée GPS est corrompue ou non disponible, ou bien qu'un plusieurs détecteurs de position sont défectueux ou bloqués. Le principe de travail vise la modification des gains du filtre Kalman en se basant sur les erreurs normalisées entre les états estimés et les observations. Pour évaluer l'architecture proposée, divers défauts de détecteurs et diverses dégradations de performance ont été mis en oeuvre et simulés. Les expériences démontrent que les solutions proposées peuvent compenser la plupart des erreurs associées aux défauts des détecteurs ou aux dégradations de performance, et que l'exactitude de positionnement qui en découle est améliorée significativement

Topics in Automotive Rollover Prevention: Robust and Adaptive Switching Strategies for Estimation and Control

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モーションコントロールへの応用のためのカルマンフィルタに関する研究 : デュアルレート・時間遅延補償・パラメータ推定

学位の種別: 課程博士審査委員会委員 : (主査)東京大学教授 堀 洋一, 東京大学教授 大崎 博之, 東京大学教授 古関 隆章, 東京大学教授 久保田 孝, 東京大学客員准教授 坂井 真一郎, 東京大学准教授 藤本 博志University of Tokyo(東京大学

Combining Sensors and Multibody Models for Applications in Vehicles, Machines, Robots and Humans

The combination of physical sensors and computational models to provide additional information about system states, inputs and/or parameters, in what is known as virtual sensing, is becoming increasingly popular in many sectors, such as the automotive, aeronautics, aerospatial, railway, machinery, robotics and human biomechanics sectors. While, in many cases, control-oriented models, which are generally simple, are the best choice, multibody models, which can be much more detailed, may be better suited to some applications, such as during the design stage of a new product

Vision Augmented State Estimation with Fault Tolerance

Obtaining accurate and reliable measurement data is really crucial for any vehicle system, especially if the system deals with maintaining safe operations of the vehicle. Conventional direct methods of obtaining such measurements by using sensors like GPS, INS, and Wheel Encoders are not reliable. The raw measurement obtained from these sensors is accompanied with bias and noise. Also, these sensors tend to underperform in certain conditions and faults, which adds to the inaccuracy and uncertainty of a system. Many different approaches exist, which intelligently fuse information from multiple sensors in order to produce reliable estimates. However, when used independently, these approaches suffer due to faults like plausibility faults, frequency faults, and error code faults, which can occur at the sensor level. This thesis therefore takes into account the development and implementation of an architecture which merges a planned fault tolerance approach to an intelligent state estimation process. The main contribution of the developed prototype is that it demonstrates the effectiveness of the developed architecture in mitigation of various faults that occur at the sensor level. This prototype lays the ground for the use of VO as an alternate source of measurement to the conventional sensors. Starting from the implementation of sensor data specific integrity checking to the implementation of Extended Kalman Filter, fault tolerance methods have been employed at different levels. A 3 DoF dynamic vehicle model has been used to enable the Extended Kalman Filter predictions. Finally, test cases were devised to validate the effectiveness of the prototype

Real-time sensor data development for smart truck drivetrains

Heavy articulated transport vehicles have a poor reputation associated with dramatic road accidents with frequent fatalities for those in automobiles. The result of this work is a formal data flow structure to enhance real-time decision-making in complex mechanical systems to increase performance capability and responsiveness to human commands. This structure recognizes the multiple layers of highly non-linear mechanical components (actuators, wheel tire & ground surfaces, controllers, power supplies, human/machine interfaces, etc.) that must operate in unison (i.e., reduce conflicts) in real-time (in milli-seconds) to enhance operator (driver) control to maximize human choice. This work contains a discussion on dependable sensor data is vital in complex systems that rely on a suite of sensors for both control as well as condition monitoring purposes as well as discussion on real-time energy distribution analysis in high momentum mechanical systems. The focus will be on tractor trucks of class 7 & 8 that are outfitted with an array of low-cost redundant sensors leveraging advances in intelligent robotic systems. This work details many topics including: Most relevant sensor types and their technologies, Designing, implementing, and maintaining a multi-sensor system using feasible industry standards, Sensor signal integrity and data flow processing for decision making, Asynchronous data flow methods for operating decision making schemes in real-time, Multiple applications to enhance tractor trucks systems with multi-sensor systems for real-time decision making.Mechanical Engineerin

Adaptive Tire Model For Dynamic Tire-Road Friction Force Estimation

As vehicle dynamics research delves deeper into better insights in performance, modeling, and vehicle controls, one area remains of utmost importance: tire and road friction forces. The vehicle’s interaction with the road remains the dominant mean of vehicle control. Ultimately, the tire-road interaction will determine the majority of the vehicle’s capabilities and as the understanding of the interface improves, so too can the performance. With more computationally intensive systems being instrumented into modern vehicle systems, one is able to observe a great deal of important vehicle states directly for the remaining vehicle information; excellent estimation techniques are providing the rest of the insights. This study looks at the possible improvements that can be observed by implementing an adaptive dynamic tire model that is physical and flexible enough to permit time varying tire performance. The tire model selected is the Average Lumped LuGre Friction Tire Model, which was originally developed from physical properties of friction and tire systems. The material presented here examines the possibility of an adaptive tire model, which can be implemented on a real-time vehicle platform. The adaptive tire model is just one section of an entire control strategy that is being developed by General Motors in partnership with the University of Waterloo. The approach allows for estimated and measured vehicle information to provide input excitation for the tire model when driven with real-world conditions that enabling tire estimations. The tire model would then provide the controller information indicating the expected tire capacity and compares it with the instantaneous loading. The adaptive tire model has been tested with flat road experimental cases and the results provided reasonable estimates. The experimentation was performed with a fully instrumented research vehicle that used in-wheel force transducers, and later repeated with a completely different non-instrumented fully electric vehicle. The concepts and investigation presented here has initiated the ground work for a real-time implementation of a full adaptive tire model. Further work is still required to evaluate the influence of a range of operating conditions, tire pressure, and of different tire types. However, the findings indicate that this approach can produce reasonable results for the specified conditions examined.1 yea

Sensors Fault Diagnosis Trends and Applications

Fault diagnosis has always been a concern for industry. In general, diagnosis in complex systems requires the acquisition of information from sensors and the processing and extracting of required features for the classification or identification of faults. Therefore, fault diagnosis of sensors is clearly important as faulty information from a sensor may lead to misleading conclusions about the whole system. As engineering systems grow in size and complexity, it becomes more and more important to diagnose faulty behavior before it can lead to total failure. In the light of above issues, this book is dedicated to trends and applications in modern-sensor fault diagnosis