Automotive Model Predictive Control [electronic resource] : Models, Methods and Applications / edited by Luigi Re, Frank Allgöwer, Luigi Glielmo, Carlos Guardiola, Ilya Kolmanovsky.

By: Re, Luigi [editor.]Contributor(s): Allgöwer, Frank [editor.] | Glielmo, Luigi [editor.] | Guardiola, Carlos [editor.] | Kolmanovsky, Ilya [editor.] | SpringerLink (Online service)Material type: TextTextLanguage: English Series: Lecture Notes in Control and Information Sciences: 402Publisher: London : Springer London, 2010Description: XIV, 290 p. 152 illus. online resourceContent type: text Media type: computer Carrier type: online resourceISBN: 9781849960717Subject(s): Engineering | Engineering | Control | Automotive EngineeringAdditional physical formats: Printed edition:: No titleDDC classification: 629.8 LOC classification: TJ212-225Online resources: Click here to access online
Contents:
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.
In: Springer eBooksSummary: 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.
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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.

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.

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