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Titlebook: Nonlinear Model Predictive Control; Theory and Algorithm Lars Grüne,Jürgen Pannek Book 20111st edition Springer-Verlag London Limited 2011

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書目名稱Nonlinear Model Predictive Control
副標(biāo)題Theory and Algorithm
編輯Lars Grüne,Jürgen Pannek
視頻videohttp://file.papertrans.cn/668/667563/667563.mp4
概述Provides researchers with a self-contained reference for nonlinear model predictive control which can support further research.Offers the student an up-to-date account of nonlinear model predictive co
叢書名稱Communications and Control Engineering
圖書封面Titlebook: Nonlinear Model Predictive Control; Theory and Algorithm Lars Grüne,Jürgen Pannek Book 20111st edition Springer-Verlag London Limited 2011
描述Nonlinear Model Predictive Control is a thorough and rigorous introduction to nonlinear model predictive control (NMPC) for discrete-time and sampled-data systems. NMPC is interpreted as an approximation of infinite-horizon optimal control so that important properties like closed-loop stability, inverse optimality and suboptimality can be derived in a uniform manner. These results are complemented by discussions of feasibility and robustness. NMPC schemes with and without stabilizing terminal constraints are detailed and intuitive examples illustrate the performance of different NMPC variants. An introduction to nonlinear optimal control algorithms gives insight into how the nonlinear optimisation routine – the core of any NMPC controller – works. An appendix covering NMPC software and accompanying software in MATLAB? and C++(downloadable from www.springer.com/ISBN) enables readers to perform computer experiments exploring the possibilities and limitations of NMPC.
出版日期Book 20111st edition
關(guān)鍵詞Control; Control Applications; Control Engineering; Control Theory; Feedback Control; Model Predictive Co
版次1
doihttps://doi.org/10.1007/978-0-85729-501-9
isbn_ebook978-0-85729-501-9Series ISSN 0178-5354 Series E-ISSN 2197-7119
issn_series 0178-5354
copyrightSpringer-Verlag London Limited 2011
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Numerical Discretization,ep size control algorithms. Furthermore, we explain how these methods can be integrated into NMPC algorithms, investigate how the numerical errors affect the stability of the NMPC controller derived from the numerical model and show which kind of robustness is needed in order to ensure a practical kind of stability.
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0178-5354 udent an up-to-date account of nonlinear model predictive coNonlinear Model Predictive Control is a thorough and rigorous introduction to nonlinear model predictive control (NMPC) for discrete-time and sampled-data systems. NMPC is interpreted as an approximation of infinite-horizon optimal control
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