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008 100311s2010 xxk| s |||| 0|eng d
020 _a9781849960717
_9978-1-84996-071-7
040 _cMX-MeUAM
050 4 _aTJ212-225
082 0 4 _a629.8
_223
100 1 _aRe, Luigi.
_eeditor.
245 1 0 _aAutomotive Model Predictive Control
_h[recurso electrónico] :
_bModels, Methods and Applications /
_cedited by Luigi Re, Frank Allgöwer, Luigi Glielmo, Carlos Guardiola, Ilya Kolmanovsky.
264 1 _aLondon :
_bSpringer London,
_c2010.
300 _aXIV, 290 p. 152 illus.
_bonline resource.
336 _atext
_btxt
_2rdacontent
337 _acomputer
_bc
_2rdamedia
338 _aonline resource
_bcr
_2rdacarrier
347 _atext file
_bPDF
_2rda
490 1 _aLecture Notes in Control and Information Sciences,
_x0170-8643 ;
_v402
505 0 _aChances 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.
520 _aAutomotive 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.
650 0 _aEngineering.
650 1 4 _aEngineering.
650 2 4 _aControl.
650 2 4 _aAutomotive Engineering.
700 1 _aAllgöwer, Frank.
_eeditor.
700 1 _aGlielmo, Luigi.
_eeditor.
700 1 _aGuardiola, Carlos.
_eeditor.
700 1 _aKolmanovsky, Ilya.
_eeditor.
710 2 _aSpringerLink (Online service)
773 0 _tSpringer eBooks
776 0 8 _iPrinted edition:
_z9781849960700
830 0 _aLecture Notes in Control and Information Sciences,
_x0170-8643 ;
_v402
856 4 0 _zLibro electrónico
_uhttp://148.231.10.114:2048/login?url=http://link.springer.com/book/10.1007/978-1-84996-071-7
596 _a19
942 _cLIBRO_ELEC
999 _c200698
_d200698