Automotive model predictive control (6 Ergebnisse)
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Taschenbuch. Zustand: Neu. Advanced Neural Network based Control for Automotive Engines | with Feed-forward Scheme and Model Predictive Control | Yujia Zhai (u. a.) | Taschenbuch | 180 S. | Englisch | 2012 | LAP LAMBERT Academic Publishing | EAN 9783847331858 | Verantwortliche Person für die EU: BoD - Books on Demand, In de Tarp…en 42, 22848 Norderstedt, info[at]bod[dot]de | Anbieter: preigu.
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Zustand: Gut. Zustand: Gut | Seiten: 304 | Sprache: Englisch | Produktart: Bücher | 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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Taschenbuch. Zustand: Neu. Automotive Model Predictive Control | Models, Methods and Applications | Luigi Del Re (u. a.) | Taschenbuch | Lecture Notes in Control and Information Sciences | xiv | Englisch | 2010 | Springer | EAN 9781849960700 | Verantwortliche Person für die EU: Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heid…elberg, juergen[dot]hartmann[at]springer[dot]com | Anbieter: preigu.
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Taschenbuch. Zustand: Neu. Druck auf Anfrage Neuware - Printed after ordering - 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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Paperback. Zustand: Brand New. 1st edition. 284 pages. 9.25x6.25x0.75 inches. In Stock.
