Automotive control has developed over the decades from an auxiliary te- nology to a key element without which the actual performances, emission, safety and consumption targets could not be met. Accordingly, automotive control has been increasing its authority and responsibility – at the price of complexity and di?cult tuning. The progressive evolution has been mainly ledby speci?capplicationsandshorttermtargets,withthe consequencethat automotive control is to a very large extent more heuristic than systematic. Product requirements are still increasing and new challenges are coming from potentially huge markets like India and China, and against this ba- ground there is wide consensus both in the industry and academia that the current state is not satisfactory. Model-based control could be an approach to improve performance while reducing development and tuning times and possibly costs. Model predictive control is a kind of model-based control design approach which has experienced a growing success since the middle of the 1980s for “slow” complex plants, in particular of the chemical and process industry. In the last decades, severaldevelopments haveallowedusing these methods also for “fast”systemsandthis hassupporteda growinginterestinitsusealsofor automotive applications, with several promising results reported. Still there is no consensus on whether model predictive control with its high requi- ments on model quality and on computational power is a sensible choice for automotive control.
Author(s): Luigi del Re, Peter Ortner, Daniel Alberer (auth.), Luigi del Re, Frank Allgöwer, Luigi Glielmo, Carlos Guardiola, Ilya Kolmanovsky (eds.)
Series: Lecture Notes in Control and Information Sciences 402
Edition: 1
Publisher: Springer-Verlag London
Year: 2010
Language: English
Pages: 290
Tags: Control; Automotive Engineering
Front Matter....Pages -
Chances and Challenges in Automotive Predictive Control....Pages 1-22
Front Matter....Pages 23-23
On Board NOx Prediction in Diesel Engines: A Physical Approach....Pages 25-36
Mean Value Engine Models Applied to Control System Design and Validation....Pages 37-52
Physical Modeling of Turbocharged Engines and Parameter Identification....Pages 53-71
Dynamic Engine Emission Models....Pages 73-87
Modeling and Model-based Control of Homogeneous Charge Compression Ignition (HCCI) Engine Dynamics....Pages 89-104
Front Matter....Pages 105-105
An Overview of Nonlinear Model Predictive Control....Pages 107-117
Optimal Control Using Pontryagin’s Maximum Principle and Dynamic Programming....Pages 119-138
On the Use of Parameterized NMPC in Real-time Automotive Control....Pages 139-149
Front Matter....Pages 151-151
An Application of MPC Starting Automotive Spark Ignition Engine in SICE Benchmark Problem....Pages 153-170
Model Predictive Control of Partially Premixed Combustion....Pages 171-181
Model Predictive Powertrain Control: An Application to Idle Speed Regulation....Pages 183-194
On Low Complexity Predictive Approaches to Control of Autonomous Vehicles....Pages 195-210
Toward a Systematic Design for Turbocharged Engine Control....Pages 211-230
An Integrated LTV-MPC Lateral Vehicle Dynamics Control: Simulation Results....Pages 231-255
MIMO Model Predictive Control for Integral Gas Engines....Pages 257-272
A Model Predictive Control Approach to Design a Parameterized Adaptive Cruise Control....Pages 273-284
Back Matter....Pages -
