Universiẗat, Diss., 2005--Kassel. / Download lizenzpflichtig.

Zugl.: Darmstadt, Techn. Universiẗat, Diss.

Zugl.: Darmstadt, Techn. University, Diss.

Zugl.: München, Techn. Univ., Diss., 2009

<p>The aim of this thesis is to improve the control of urea injection at the combined power and heating plant Framtidens Gärstad (FTG). Tekniska Verken i Linköping AB is the owner of the plant.</p><p>The FTG plant consists of a boiler where garbage combustion is done. From the combustion nitrogen oxides are emitted. These nitrogen oxides are hazardous to the environment. To reduce the nitrogen oxides, injection of urea into the boiler is used. Urea is an organic compound of carbon, nitrogen, oxygen and hydrogen. When urea reacts with nitrogen oxides they transform into harmless nitrogen gas and water. In the plant the urea is injected by six lances.</p><p>The control of the urea injection at FTG could be improved which would save some money for the company. This is the main reason for the aim of this thesis. It is the control of the total flow of urea to the lances that is studied in this work.</p><p>Some literature about reduction of nitrogen oxides is studied, especially reduction using urea injection. There are a lot of factors that affect how good the reduction becomes. The most important factors are the amount of urea being used and the temperature of the flue gases where the reaction with urea takes place. A model with these two factors as inputs and the content of nitrogen oxides as output is derived. This is done with experiments in the boiler and system identification. The system is modelled as a linear system.</p><p>The proposal about the improved control uses the temperature from a temperature measurement just below the urea injection in the boiler. This is a parameter that the existing control does not use. The temperature is divided into three intervals. For each interval different parameters for the function of the system and the nitrogen oxide controller are used. The nitrogen oxide controller in the proposed control is derived with help from a new method of controldesign called AMIGO.</p><p>The identification models gave good results in two of the temperature intervals. The result for the third interval was not so good. This is probably due to lack of good data.</p><p>The proposed control structure could for practical reasons not be tested online but preliminary tests using measurement data gave qualitatively reasonable results.</p><p>In order to improve the results temperature dependence has to be treated more systematically.</p>

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.

Techn. Hochsch., Diss., 2003--Aachen.

The aim of this thesis is to improve the control of urea injection at the combined power and heating plant Framtidens Gärstad (FTG). Tekniska Verken i Linköping AB is the owner of the plant. The FTG plant consists of a boiler where garbage combustion is done. From the combustion nitrogen oxides are emitted. These nitrogen oxides are hazardous to the environment. To reduce the nitrogen oxides, injection of urea into the boiler is used. Urea is an organic compound of carbon, nitrogen, oxygen and hydrogen. When urea reacts with nitrogen oxides they transform into harmless nitrogen gas and water. In the plant the urea is injected by six lances. The control of the urea injection at FTG could be improved which would save some money for the company. This is the main reason for the aim of this thesis. It is the control of the total flow of urea to the lances that is studied in this work. Some literature about reduction of nitrogen oxides is studied, especially reduction using urea injection. There are a lot of factors that affect how good the reduction becomes. The most important factors are the amount of urea being used and the temperature of the flue gases where the reaction with urea takes place. A model with these two factors as inputs and the content of nitrogen oxides as output is derived. This is done with experiments in the boiler and system identification. The system is modelled as a linear system. The proposal about the improved control uses the temperature from a temperature measurement just below the urea injection in the boiler. This is a parameter that the existing control does not use. The temperature is divided into three intervals. For each interval different parameters for the function of the system and the nitrogen oxide controller are used. The nitrogen oxide controller in the proposed control is derived with help from a new method of controldesign called AMIGO. The identification models gave good results in two of the temperature intervals. The result for the third interval was not so good. This is probably due to lack of good data. The proposed control structure could for practical reasons not be tested online but preliminary tests using measurement data gave qualitatively reasonable results. In order to improve the results temperature dependence has to be treated more systematically.

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

Die vorliegende Arbeit befasst sich mit datengetriebenen Modellierungsansätzen in der Gebäudetechnik zum Zweck des Entwurfs modellprädiktiver Regler. Dabei widmet sich der erste Teil (Kapitel 2 und 3) im Wesentlichen den theoretischen Grundlagen der Systemidentifikation wohingegen im zweiten Teil (Kapitel 4 bis 6) praktische Anwendungen der Identifikation sowie die Implementierung modellprädiktiver Regler im Gebäudebereich im Vordergrund stehen. Die Grundlage der Arbeit stellt die Systemidentifikation mittels der Prädiktionsfehlerminimierung (Prediction Error Minimization – PEM) dar, deren Basis stochastische Modelle dynamischer Systeme bilden. Daher wird ausgehend von den unterschiedlichen Darstellungsformen stochastischer linearer zeitinvarianter Systeme (LTI-Systeme) die Problematik der optimalen Prädiktion thematisiert, wobei ein allgemeiner Ausdruck zur Berechnung der Mehrschrittprädiktion von LTI-Systemen hergeleitet wird. Anschließend werden Implementierungsaspekte von Mehr- bzw. Multischritt-PEM-Verfahren diskutiert und der Schätzer im Frequenzbereich analysiert, wobei sich verglichen mit der 1-Schritt-PEM eine erwartungsgemäß andere Wichtung des geschätzten Modells im Frequenzbereich ergibt. Die aus diesen Untersuchungen gewonnen Identifizierbarkeitsbedingungen entsprechen im offenen Regelkreis den bekannten Bedingungen der 1-Schritt-PEM, wohingegen sich im geschlossenen Regelkreis eine restriktivere Bedingung ergibt. Die weiterhin durchgeführte Genauigkeitsanalyse führt letztlich zu einem Ausdruck für die Abschätzung der Schätzfehlerkovarianzmatrix der Modellparameter, welche wiederum für die Varianz- bzw. Konfidenzschätzung anderer Systemmerkmale genutzt werden kann. In der Gebäudetechnik spielen nichtlineare Modellansätze eine wichtige Rolle. Für diese ist die explizite Berechnung der optimalen Prädiktion, und damit eine Identifikation mittels PEM, in der Regel problematisch bzw. nicht möglich. In der Arbeit werden daher zwei nichtlineare Modellstrukturen vorgeschlagen, für welche entsprechende Ausdrücke angegeben werden können, sodass eine direkte Anwendung der PEM ermöglicht wird. Dabei hat sich im Anwendungsteil der Arbeit herausgestellt, dass einer dieser Ansätze sinnvoll in der Gebäudetechnik genutzt werden kann. Aufgrund der Ähnlichkeit zum linearen Fall können zudem die Ideen, die typischerweise für die Parametrierung stochastischer LTI Systeme genutzt werden, auf den nichtlinearen Fall übertragen werden. Im Anwendungsteil der Arbeit wird dann die Nutzung der PEM im Gebäudebereich anhand praktisch aufgenommener Messdaten einiger Demonstratoren veranschaulicht. Es werden Modell- und Identifikationsansätze für die Schätzung von thermischen Raummodellen sowie einer Gastherme und einer Wärmepumpe vorgeschlagen, wobei plausible Identifikationsergebnisse erzielt werden konnten. Weiterhin wird im Rahmen der Identifikation von thermischen Raummodellen auch auf die wichtige Problematik nicht messbarer Störungen (z. B. Raumbelegung sowie Wärmeströme aus Nachbarräumen) eingegangen und mögliche Lösungsansätze aufgezeigt. Aufgrund der umfangreichen Sensorausstattung einiger Demonstratoren konnte zudem ein direkter Vergleich eines LTI-Modells mit dem obig genannten nichtlinearen Modellansatz durchgeführt werden, wobei eine vergleichbare Prädiktionsgüte beider Modellierungsansätze festgestellt wurde. Im letzten Teil wird dann der Entwurf modellprädiktiver Regler anhand zweier typischer Beispiele aus der Gebäudetechnik veranschaulicht. Für die Demonstration des Vorgehens werden Regelstreckenmodelle mittels der Modelica Buildings Bibliothek entworfen und dann für die Generierung synthetischer Daten verwendet, welche zur Identifikation von Modellen für den MPC Entwurf verwendet werden. Die identifizierten Modelle sind in beiden Fällen in der Lage das dynamische Verhalten des Modelica Prozessmodells hinreichend gut vorherzusagen, was die Sinnhaftigkeit des gewählten Modellierungs- bzw. Identifikationsansatzes untermauert. Aufbauend wird dann für beide Beispiele auf die Formulierung einer prädiktiven Regelungsstrategie eingegangen, wobei spezielle Modelleigenschaften ausgenutzt werden. Für beide Beispiele konnten simulativ Vorteile gegenüber einer konventionellen Regelungsstrategie nachgewiesen werden.