Luigi del Re, Frank Allgower, Luigi Glielmo, Carlos Guardiola, and Ilya Kolmanovsky (Eds.) Automotive Model Predictive Control Models, Methods and Applications Springer
Contents 1 Chances and Challenges in Automotive Predictive Control 1 Luigi del Re, Peter Ortner, Daniel Alberer 1.1 Introduction: The Rationale 1 1.2 Alternatives for Modeling 4 1.2.1 First Principles Models 5 1.2.2 Data-only Models 6 1.2.3 Advanced Use of Data 7 1.3 Alternatives for Optimization 9 1.3.1 Basic Algorithmic Approaches 9 1.3.2 Coping with Nonlinearity 12 1.4 Chances: State and Outlook 15 1.5 Conclusions 19 References 19 Part I: Models 2 On Board NOx Prediction in Diesel Engines: A Physical Approach 25 Jean Arregle, J. Javier Lopez, Carlos Guardiola, Christelle Monin 2.1 Introduction 25 2.2 Main Physical/Chemical Mechanisms of NOx Formation/Destruction 26 2.2.1 NOx Re-burning 27 2.2.2 NOx Formation in LTC Conditions 29 2.3 Mechanisms and Model Sensitivity 30 2.3.1 Structure of Physically-based NOx Models 30 2.3.2 Flame Temperature Determination 31 2.4 Input Parameters Accuracy 33
VIII Contents... 47 2.4.1 Intake Air Mass Flow Rate Accuracy 33 2.4.2 Air + EGR Mixture Temperature and Oxygen Fraction 33 2.5 Conclusions 34 References 35 3 Mean Value Engine Models Applied to Control System Design and Validation 37 Pierre Olivier Calendini, Stefan Breuer 3.1 State of the Art Mean Value Engine Model 38 3.2 System Model Structure as a Response to the Requirements 39 3.2.1 Bond Graph Applied to Mean Value Engine Models 39 3.2.2 Naturally Aspirated and Turbocharged Engine in Bond Graph Structure 40 3.3 Basic Blocs for Building Mean Value Models 42 3.3.1 The Volume Bloc 42 3.3.2 The Gas Exchange Bloc 43 3.3.3 Heat Exchange Models 43 3.3.4 Combustion Model Possibilities 43 3.3.5 Environment Model 44 3.4 Application Example: Choice of an Air Loop Control Strategy 45 3.4.1 Implementation of the Robustness Simulation 3.4.2 Results of the Robustness Simulations 49 3.5 Conclusions 50 References 51 4 Physical Modeling of Turbocharged Engines and Parameter Identification 53 Lars Eriksson, Johan Wahlstrom, Markus Klein 4.1 Introduction 53 4.2 MVEM Modeling 54 4.2.1 Library Development 54 4.2.2 Building Blocks: Component Models 55 4.2.3 The Engine Cylinders: Flow, Temperature, and Torque 59 4.2.4 Implementation Examples 60 4.3 Modeling of a Diesel Engine with EGR/VGT 60 4.3.1 Experimental Data 61 4.3.2 Minimum Number of States 64 4.3.3 Model Extensions 64 4.4 Gray-Box Models and Identification 65 4.5 Conclusions 68 References 69
Contents IX 5 Dynamic Engine Emission Models 73 Markus Hirsch, Klaus Oppenauer, Luigi del Re 5.1 Introduction 73 5.2 Data-based Model Identification 75 5.3 Mean Value Emission Model 76 5.3.1 Input Selection 76 5.3.2 Model Structure 77 5.3.3 Parameter Identification 77 5.3.4 Regressor Selection 78 5.3.5 Realization and Results 78 5.4 Crank Angle Based Emission Model 79 5.4.1 Workflow 79 5.4.2 1-zone Process Calculation 80 5.4.3 2-zone Model 80 5.4.4 Emission Models 81 5.4.5 Model Development and Verification 82 5.5 Data for Identification: Input Design 82 5.6 Limitations 85 5.7 Summary 85 References 86 6 Modeling and Model-based Control of Homogeneous Charge Compression Ignition (HCCI) Engine Dynamics 89 Rolf Johansson, Per Timestdl, Anders Widd 6.1 Introduction 89 6.2 HCCI Modeling 91 6.2.1 Fuel Modeling 92 6.2.2 Auto-ignition Modeling 93 6.2.3 Thermal Modeling and Auto-ignition 93 6.3 Experiments 98 6.3.1 Model Predictive Control 98 6.4 Conclusions 99 References 100 Part II: Methods 107... 7 An Overview of Nonlinear Model Predictive Control Lalo Magni, Riccardo Scattolini 7.1 Introduction 107 7.2 Problem Formulation and State-feedback NMPC Control Law 108 7.2.1 Feasibility and Stability in Nominal Conditions... 109 7.2.2 The Robustness Problem 109 7.3 Output Feedback and Tracking Ill
X Contents 7.3.1 Output Feedback Ill 7.3.2 Tracking 112 7.4 Implementation Problems and Alternative Approaches 113... References 115 8 Optimal Control Using Pontryagin's Maximum Principle and Dynamic Programming 119 Bart Saerens, Moritz Diehl, Eric Van den Bulck 8.1 Introduction 119 8.2 Optimal Control 120 8.2.1 Pontryagin's Maximum Principle 123 8.2.2 Dynamic Programming 124 8.3 Vehicle and Powertrain Model 124 8.3.1 Vehicle and Driveline Model 125 8.3.2 Engine Model 125 8.4 Minumum-fuel Acceleration with the Maximum Principle 129 8.5 Minumum-fuel Acceleration with Dynamic Programming 132 8.6 Discussion of the Results 134 8.6.1 Comparison between the Maximum Principle and Dynamic Programming 135 8.6.2 Comparison with Other Research 135 8.7 Conclusions 136 References 137 9 On the Use of Parameterized NMPC in Real-time Automotive Control 139 Mazen Alamir, Andre Murilo, Richard Fiirhapter, Peter Ortner Rachid Amari, Paolina Tona, 9.1 Introduction 140 9.2 The Parameterized NMPC: Definitions and Notation 141 9.3 Example 1: Diesel Engine Air Path Control 142 9.4 Example 2: Automated Manual Transmission Control 145 9.5 Conclusion 148 References 148 Part III: Applications 10 An Application of MPC Starting Automotive Spark Ignition Engine in SICE Benchmark Problem 153 Akira Ohata, Masaki Yamakita 10.1 Introduction 153 10.2 Control Design Strategy in MBD 159 10.3 Benchmark Problem 161
Contents XI 10.4 Application of MPC 165 10.5 Summary 169 References 169 11 Model Predictive Control of Partially Premixed Combustion 171. Per Tunestdl, Magnus Lewander 11.1 Introduction 171 11.2 Experimental Setup 172 11.3 PPC Definition 172 11.4 Control 174 11.4.1 Control Design 174 11.5 Results 175 11.5.1 Response to EGR. Disturbance 175 11.5.2 Response to Load Changes 177 11.5.3 Response to Speed Changes 177 11.6 Discussion 179 11.7 Conclusions 180 References 181 12 Model Predictive Powertrain Control: An Application to Idle Speed Regulation 183 Stefano Di Cairano, Diana Yanakiev, Alberto Bemporad, Ilya Kolrnanovsky, Davar Hrovat 12.1 Introduction 183 12.2 Engine Model for Idle Speed Control 184 12.3 Control-oriented Model and Controller Design 185 12.4 Controller Synthesis and Refinement 189 12.4.1 Feedback Law Synthesis and Functional Assessment 189 12.4.2 Prediction Model Refinement 190 12.5 Experimental Validation 191 12.6 Conclusions 193 References 193 13 On Low Complexity Predictive Approaches to Control of Autonomous Vehicles 195 Paolo Falcone, Francesco Borrelli, Eric H. Tseng, Davor Hrovat 13.1 Introduction to Autonomous Guidance Systems 195 13.2 Vehicle Modeling 198 13.3 Low Complexity Predictive Path Following 201 13.3.1 Two Levels Autonomous Path Following 201 13.3.2 Single Level Autonomous Path Following 204 13.4 Results 206
XII Contents 13.5 Conclusions 209 References 209 14 Toward a Systematic Design for Turbocharged Engine Control 211 Greg Stewart, Francesco Borrelli, Jaroslav Pekar, David Germann, Daniel Pachner, Dejan Kihas 14.1 Introduction 211 14.2 Engine Control Requirements 213 14.2.1 Steady-state Engine Calibration 213 14.2.2 Control Functional Development 213 14.2.3 Functional Testing 216 14.2.4 Software Development 216 14.2.5 Integration 216 14.2.6 Calibration 216 14.2.7 Certification 216 14.2.8 Release and Post-release Support 217 14.2.9 Iteration Loops 217 14.3 Modeling and Control for Turbocharged Engines 218 14.3.1 Modeling 219 14.4 Model Predictive Control and Computational Complexity 223 14.4.1 Explicit Predictive Control 223 14.4.2 On the Complexity of Explicit MPC Control Laws 226 14.5 Summary and Conclusions 228 References 229 15 An Integrated LTV-MPC Lateral Vehicle Dynamics Control: Simulation Results 231 Giovanni Palmieri, Osvaldo Barbarisi, Stefano Scala, Luigi Glielmo 15.1 Introduction 231 15.2 Full Vehicle Model 234 15.3 Lateral Vehicle Dynamic Control Strategy 235 15.3.1 Reference Signals 236 15.3.2 Estimation of Tire Variables 237 15.3.3 Supervisor 240 15.3.4 Model Predictive Control 241 15.3.5 An Alternative 2PI Regulator 244 15.4 A Reduced Model for Slip Control 245 15.5 A Slip Control Strategy 246 15.5.1 Feedback Action 247 15.6 Simulation Results 248 15.7 Conclusions 253 References 254
Contents XIII 16 MIMO Model Predictive Control for Integral Gas Engines 257 Jakob Angeby, Matthias Huschenbett, Daniel Alberer 16.1 Introduction 257 16.2 System Description 258 16.3 Problem Statement 261 16.4 Model Predictive Control 262 16.5 Implementation 263 16.5.1 Objective Function 264 16.5.2 Model Derivation 264 16.6 Model Extensions 266 16.7 Real-time MPC 267 16.8 Results 268 16.9 Conclusions 271 References 271 17 A Model Predictive Control Approach to Design a Parameterized Adaptive Cruise Control 273 Gerrit J.L. Naus, Jeroen Ploeg, M.J.G. Van de Molengraft, W.P.M.H. Heemels, Maarten Steinbuch 17.1 Introduction 273 17.2 Problem Formulation 275 17.2.1 Quantification Measures 275 17.2.2 Parameterization 275 17.3 Model Predictive Control Problem Setup 276 17.3.1 Modeling 276 17.3.2 Control Objectives and Constraints 277 17.3.3 Control Problem / Cost Criterion Formulation... 278 17.4 Controller Design 279 17.4.1 Parameterization 279 17.4.2 Implementation Issues 280 17.4.3 Results 281 17.5 Conclusions and Future Work 282 References 283 Author Index 285