In modern automotive industry, the state-of-art technology of fuel injection controllers utilizes feed-forward control with a mass airflow sensor located upstream of the throttle, plus a proportional and integral (PI) type feedback control. The feed-forward control is simply implemented with look-up tables, which requires a laborious process of calibration and tuning. With the development of micro-controllers for engine control units (ECU), a variety of advanced control schemes has been introduced to automotive industry. This research work, firstly, investigated neural network based feed-forward control method to improve the performance of fuel injector. In addition, based on the air/fuel ratio model developed, a nonlinear model predictive control scheme is implemented successfully, and the control performance and robustness are evaluated by introducing system uncertainties.
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In modern automotive industry, the state-of-art technology of fuel injection controllers utilizes feed-forward control with a mass airflow sensor located upstream of the throttle, plus a proportional and integral (PI) type feedback control. The feed-forward control is simply implemented with look-up tables, which requires a laborious process of calibration and tuning. With the development of micro-controllers for engine control units (ECU), a variety of advanced control schemes has been introduced to automotive industry. This research work, firstly, investigated neural network based feed-forward control method to improve the performance of fuel injector. In addition, based on the air/fuel ratio model developed, a nonlinear model predictive control scheme is implemented successfully, and the control performance and robustness are evaluated by introducing system uncertainties.
Dr. Yujia Zhai received his M.Sc and Ph.D. from University of Liverpool and Liverpool John Moores University, UK, in 2004 and 2009, respectively. He worked on different international research projects on Dynamical System Identification and Nonlinear Control before he joined Xi'an Jiao Tong-Liverpool University in August, 2011 as a senior lecturer.
Les informations fournies dans la section « A propos du livre » peuvent faire référence à une autre édition de ce titre.
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Taschenbuch. Etat : Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -In modern automotive industry, the state-of-art technology of fuel injection controllers utilizes feed-forward control with a mass airflow sensor located upstream of the throttle, plus a proportional and integral (PI) type feedback control. The feed-forward control is simply implemented with look-up tables, which requires a laborious process of calibration and tuning. With the development of micro-controllers for engine control units (ECU), a variety of advanced control schemes has been introduced to automotive industry. This research work, firstly, investigated neural network based feed-forward control method to improve the performance of fuel injector. In addition, based on the air/fuel ratio model developed, a nonlinear model predictive control scheme is implemented successfully, and the control performance and robustness are evaluated by introducing system uncertainties. 180 pp. Englisch. N° de réf. du vendeur 9783847331858
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Etat : New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Autor/Autorin: Zhai YujiaDr. Yujia Zhai received his M.Sc and Ph.D. from University of Liverpool and Liverpool John Moores University, UK, in 2004 and 2009, respectively. He worked on different international research projects on Dynamical System Id. N° de réf. du vendeur 5510632
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Taschenbuch. Etat : 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 Tarpen 42, 22848 Norderstedt, info[at]bod[dot]de | Anbieter: preigu. N° de réf. du vendeur 106669369
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Taschenbuch. Etat : Neu. This item is printed on demand - Print on Demand Titel. Neuware -In modern automotive industry, the state-of-art technology of fuel injection controllers utilizes feed-forward control with a mass airflow sensor located upstream of the throttle, plus a proportional and integral (PI) type feedback control. The feed-forward control is simply implemented with look-up tables, which requires a laborious process of calibration and tuning. With the development of micro-controllers for engine control units (ECU), a variety of advanced control schemes has been introduced to automotive industry. This research work, firstly, investigated neural network based feed-forward control method to improve the performance of fuel injector. In addition, based on the air/fuel ratio model developed, a nonlinear model predictive control scheme is implemented successfully, and the control performance and robustness are evaluated by introducing system uncertainties.VDM Verlag, Dudweiler Landstraße 99, 66123 Saarbrücken 180 pp. Englisch. N° de réf. du vendeur 9783847331858
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Taschenbuch. Etat : Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - In modern automotive industry, the state-of-art technology of fuel injection controllers utilizes feed-forward control with a mass airflow sensor located upstream of the throttle, plus a proportional and integral (PI) type feedback control. The feed-forward control is simply implemented with look-up tables, which requires a laborious process of calibration and tuning. With the development of micro-controllers for engine control units (ECU), a variety of advanced control schemes has been introduced to automotive industry. This research work, firstly, investigated neural network based feed-forward control method to improve the performance of fuel injector. In addition, based on the air/fuel ratio model developed, a nonlinear model predictive control scheme is implemented successfully, and the control performance and robustness are evaluated by introducing system uncertainties. N° de réf. du vendeur 9783847331858
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