Automotive model predictive control : models, methods and applications / edited by Luigi del Re [and others].

Includes bibliographical references and index.

1. Chances and Challenges in Automotive Predictive Control / Luigi del Re, Peter Ortner, Daniel Alberer -- 1.1. Introduction: The Rationale -- 1.2. Alternatives for Modeling -- 1.2.1. First Principles Models -- 1.2.2. Data-only Models -- 1.2.3. Advanced Use of Data -- 1.3. Alternatives for Optimization -- 1.3.1. Basic Algorithmic Approaches -- 1.3.2. Coping with Nonlinearity -- 1.4. Chances: State and Outlook -- 1.5. Conclusions -- Part I. Models -- 2. On Board NOx Prediction in Diesel Engines: A Physical Approach / Jean Arregle, J. Javier Lopez, Carlos Guardiola, B Christelle Monin -- 2.1. Introduction -- 2.2. Main Physical /Chemical Mechanisms of NOx Formation /Destruction -- 2.2.1. NOx Re-burning -- 2.2.2. NOx Formation in LTC Conditions -- 2.3. Mechanisms and Model Sensitivity -- 2.3.1. Structure of Physically-based NOx Models -- 2.3.2. Flame Temperature Determination -- 2.4. Input Parameters Accuracy -- 2.4.1. Intake Air Mass Flow Rate Accuracy -- 2.4.2. Air + EGR Mixture Temperature and Oxygen Fraction -- 2.5. Conclusions -- 3. Mean Value Engine Models Applied to Control System Design and Validation / Pierre Olivier Calendini, Stefan Breuer -- 3.1. State of the Art Mean Value Engine Model -- 3.2. System Model Structure as a Response to the Requirements -- 3.2.1. Bond Graph Applied to Mean Value Engine Models -- 3.2.2. Naturally Aspirated and Turbocharged Engine in Bond Graph Structure -- 3.3. Basic Blocs for Building Mean Value Models -- 3.3.1. The Volume Bloc -- 3.3.2. The Gas Exchange Bloc -- 3.3.3. Heat Exchange Models -- 3.3.4. Combustion Model Possibilities -- 3.3.5. Environment Model -- 3.4. Application Example: Choice of an Air Loop Control Strategy -- 3.4.1. Implementation of the Robustness Simulation -- 3.4.2. Results of the Robustness Simulations -- 3.5. Conclusions -- 4. Physical Modeling of Turbocharged Engines and Parameter Identification / Lars Eriksson, Johan Wahlstrom, Markus Klein -- 4.1. Introduction -- 4.2. MVEM Modeling -- 4.2.1. Library Development -- 4.2.2. Building Blocks: Component Models -- 4.2.3. The Engine Cylinders: Flow, Temperature, and Torque -- 4.2.4. Implementation Examples -- 4.3. Modeling of a Diesel Engine with EGR /VGT -- 4.3.1. Experimental Data -- 4.3.2. Minimum Number of States -- 4.3.3. Model Extensions -- 4.4. Gray-Box Models and Identification -- 4.5. Conclusions -- 5. Dynamic Engine Emission Models / Markus Hirsch, Klaus Oppenauer, Luigi del Re -- 5.1. Introduction -- 5.2. Data-based Model Identification -- 5.3. Mean Value Emission Model -- 5.3.1. Input Selection -- 5.3.2. Model Structure -- 5.3.3. Parameter Identification -- 5.3.4. Regressor Selection -- 5.3.5. Realization and Results -- 5.4. Crank Angle Based Emission Model -- 5.4.1. Workflow -- 5.4.2. 1-zone Process Calculation -- 5.4.3. 2-zone Model -- 5.4.4. Emission Models -- 5.4.5. Model Development and Verification -- 5.5. Data for Identification: Input Design -- 5.6. Limitations -- 5.7. Summary -- 6. Modeling and Model-based Control of Homogeneous Charge Compression Ignition (HCCI) Engine Dynamics / Rolf Johansson, Per Tunestal, Anders Widd -- 6.1. Introduction -- 6.2. HCCI Modeling -- 6.2.1. Fuel Modeling -- 6.2.2. Auto-ignition Modeling -- 6.2.3. Thermal Modeling and Auto-ignition -- 6.3. Experiments -- 6.3.1. Model Predictive Control -- 6.4. Conclusions -- Part II. Methods -- 7. An Overview of Nonlinear Model Predictive Control / Lalo Magni, Riccardo Scattolini -- 7.1. Introduction -- 7.2. Problem Formulation and State-feedback NMPC Control Law -- 7.2.1. Feasibility and Stability in Nominal Conditions -- 7.2.2. The Robustness Problem -- 7.3. Output Feedback and Tracking -- 7.3.1. Output Feedback -- 7.3.2. Tracking -- 7.4. Implementation Problems and Alternative Approaches -- 8. Optimal Control Using Pontryagin's Maximum Principle and Dynamic Programming / Bart Saerens, Moritz Diehl, Eric Van den Bulck -- 8.1. Introduction -- 8.2. Optimal Control -- 8.2.1. Pontryagin's Maximum Principle -- 8.2.2. Dynamic Programming -- 8.3. Vehicle and Powertrain Model -- 8.3.1. Vehicle and Driveline Model -- 8.3.2. Engine Model -- 8.4. Minimum-fuel Acceleration with the Maximum Principle -- 8.5. Minimum-fuel Acceleration with Dynamic Programming -- 8.6. Discussion of the Results -- 8.6.1. Comparison between the Maximum Principle and Dynamic Programming -- 8.6.2. Comparison with Other Research -- 8.7. Conclusions -- 9. On the Use of Parameterized NMPC in Real-time Automotive Control (Mazen Alamir, Andre Murilo, Rachid Amari, Paolina Tona, Richard Furhapter, Peter Ortner( -- 9.1. Introduction -- 9.2. The Parameterized NMPC: Definitions and Notation -- 9.3. Example 1: Diesel Engine Air Path Control -- 9.4. Example 2: Automated Manual Transmission Control -- 9.5. Conclusion -- Part III. Applications --

10. An Application of MPC Starting Automotive Spark Ignition Engine in SICE Benchmark Problem / Akira Ohata, Masaki Yamakita -- 10.1. Introduction -- 10.2. Control Design Strategy in MBD -- 10.3. Benchmark Problem -- 10.4. Application of MPC -- 10.5. Summary -- 11. Model Predictive Control of Partially Premixed Combustion / Per Tunestal, Magnus Lewander -- 11.1. Introduction -- 11.2. Experimental Setup -- 11.3. PPC Definition -- 11.4. Control -- 11.4.1. Control Design -- 11.5. Results -- 11.5.1. Response to EGR Disturbance -- 11.5.2. Response to Load Changes -- 11.5.3. Response to Speed Changes -- 11.6. Discussion -- 11.7. Conclusions -- 12. Model Predictive Powertrain Control: An Application to Idle Speed Regulation / Stefano Di Cairano, Diana Yanakiev, Alberto Bemporad, Ilya Kolmanovsky, Davor Hrovat -- 12.1. Introduction -- 12.2. Engine Model for Idle Speed Control -- 12.3. Control-oriented Model and Controller Design -- 12.4. Controller Synthesis and Refinement -- 12.4.1. Feedback Law Synthesis and Functional Assessment -- 12.4.2. Prediction Model Refinement -- 12.5. Experimental Validation -- 12.6. Conclusions -- 13. On Low Complexity Predictive Approaches to Control of Autonomous Vehicles / Paolo Falcone, Francesco Borrelli, Eric H. Tseng, Davor Hrovat -- 13.1. Introduction to Autonomous Guidance Systems -- 13.2. Vehicle Modeling -- 13.3. Low Complexity Predictive Path Following -- 13.3.1. Two Levels Autonomous Path Following -- 13.3.2. Single Level Autonomous Path Following -- 13.4. Results -- 13.5. Conclusions -- 14. Toward a Systematic Design for Turbocharged Engine Control / Greg Stewart, Francesco Borrelli, Jaroslav Pekar, David Germann, Daniel Pachner, Dejan Kihas -- 14.1. Introduction -- 14.2. Engine Control Requirements -- 14.2.1. Steady-state Engine Calibration -- 14.2.2. Control Functional Development -- 14.2.3. Functional Testing -- 14.2.4. Software Development -- 14.2.5. Integration -- 14.2.6. Calibration -- 14.2.7. Certification -- 14.2.8. Release and Post-release Support -- 14.2.9. Iteration Loops -- 14.3. Modeling and Control for Turbocharged Engines -- 14.3.1. Modeling -- 14.4. Model Predictive Control and Computational Complexity -- 14.4.1. Explicit Predictive Control -- 14.4.2. On the Complexity of Explicit MPC Control Laws -- 14.5. Summary and Conclusions -- 15. An Integrated LTV-MPC Lateral Vehicle Dynamics Control: Simulation Results / Giovanni Palmieri, Osvaldo Barbarisi, Stefano Scala, Luigi Glielmo -- 15.1. Introduction -- 15.2. Full Vehicle Model -- 15.3. Lateral Vehicle Dynamic Control Strategy -- 15.3.1. Reference Signals -- 15.3.2. Estimation of Tire Variables -- 15.3.3. Supervisor -- 15.3.4. Model Predictive Control -- 15.3.5. An Alternative 2PI Regulator -- 15.4. A Reduced Model for Slip Control -- 15.5. A Slip Control Strategy -- 15.5.1. Feedback Action -- 15.6. Simulation Results -- 15.7. Conclusions -- 16. MIMO Model Predictive Control for Integral Gas Engines / Jakob Angeby, Matthias Huschenbett, Daniel Alberer -- 16.1. Introduction -- 16.2. System Description -- 16.3. Problem Statement -- 16.4. Model Predictive Control -- 16.5. Implementation -- 16.5.1. Objective Function -- 16.5.2. Model Derivation -- 16.6. Model Extensions -- 16.7. Real-time MPC -- 16.8. Results -- 16.9. Conclusions -- 17. A Model Predictive Control Approach to Design a Parameterized Adaptive Cruise Control / Gerrit J.L. Naus, Jeroen Ploeg, M.J.G. Van de Molengraft, W.P.M.H. Heemels, Maarten Steinbuch -- 17.1. Introduction -- 17.2. Problem Formulation -- 17.2.1. Quantification Measures -- 17.2.2. Parameterization -- 17.3. Model Predictive Control Problem Setup -- 17.3.1. Modeling -- 17.3.2. Control Objectives and Constraints -- 17.3.3. Control Problem / Cost Criterion Formulation -- 17.4. Controller Design -- 17.4.1. Parameterization -- 17.4.2. Implementation Issues -- 17.4.3. Results -- 17.5. Conclusions and Future Work.

Machine converted from AACR2 source record.