Chances and Challenges in Automotive Predictive Control -- Chances and Challenges in Automotive Predictive Control -- I: Models -- On Board NOx Prediction in Diesel Engines: A Physical Approach -- Mean Value Engine Models Applied to Control System Design and Validation -- Physical Modeling of Turbocharged Engines and Parameter Identification -- Dynamic Engine Emission Models -- Modeling and Model-based Control of Homogeneous Charge Compression Ignition (HCCI) Engine Dynamics -- II: Methods -- An Overview of Nonlinear Model Predictive Control -- Optimal Control Using Pontryagin’s Maximum Principle and Dynamic Programming -- On the Use of Parameterized NMPC in Real-time Automotive Control -- III: Applications -- An Application of MPC Starting Automotive Spark Ignition Engine in SICE Benchmark Problem -- Model Predictive Control of Partially Premixed Combustion -- Model Predictive Powertrain Control: An Application to Idle Speed Regulation -- On Low Complexity Predictive Approaches to Control of Autonomous Vehicles -- Toward a Systematic Design for Turbocharged Engine Control -- An Integrated LTV-MPC Lateral Vehicle Dynamics Control: Simulation Results -- MIMO Model Predictive Control for Integral Gas Engines -- A Model Predictive Control Approach to Design a Parameterized Adaptive Cruise Control.

The proceedings of the Automotive Model Predictive Control: Models, Methods and Applications workshop investigates whether constrained predictive control is reasonable in automotive control and what is necessary for its application. The workshop, held at the University of Linz on 9th – 10th February 2009 brought together workers from academia and industry from three key automotive branches: modeling, control and the application. The workshop included three keynote presentations, each of them contributing to the solution of an essential question. • Which problems in automotive applications need constrained optimal control? • Models of emissions for modern engines for model based control? • Industrial methods and requirements for control schemes?The results of testing control strategies on a dynamical engine test bench give a feeling for the necessary computing power, the model plant mismatch, etc. and thus for the real application of control laws in production cars.