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Titlebook: Computationally Efficient Model Predictive Control Algorithms; A Neural Network App Maciej ?awryńczuk Book 2014 Springer International Publ

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發(fā)表于 2025-3-21 18:32:26 | 只看該作者 |倒序瀏覽 |閱讀模式
書目名稱Computationally Efficient Model Predictive Control Algorithms
副標(biāo)題A Neural Network App
編輯Maciej ?awryńczuk
視頻videohttp://file.papertrans.cn/234/233260/233260.mp4
概述Presents recent research in Computationally Efficient Model Predictive Control Algorithms.Focuses on a Neural Network Approach for Model Predictive Control.Written by an expert in the field
叢書名稱Studies in Systems, Decision and Control
圖書封面Titlebook: Computationally Efficient Model Predictive Control Algorithms; A Neural Network App Maciej ?awryńczuk Book 2014 Springer International Publ
描述.This book thoroughly discusses computationally efficient (suboptimal) Model Predictive Control (MPC) techniques based on neural models. The subjects treated include:.·???????? A few types of suboptimal MPC algorithms in which a linear approximation of the model or of the predicted trajectory is successively calculated on-line and used for prediction..·???????? Implementation details of the MPC algorithms for feed forward perceptron neural models, neural Hammerstein models, neural Wiener models and state-space neural models..·???????? The MPC algorithms based on neural multi-models (inspired by the idea of predictive control)..·???????? The MPC algorithms with neural approximation with no on-line linearization..·???????? The MPC algorithms with guaranteed stability and robustness..·???????? Cooperation between the MPC algorithms and set-point optimization..Thanks to linearization (or neural approximation), the presented suboptimal algorithms do not require demanding on-line nonlinear optimization. The presented simulation results demonstrate high accuracy and computational efficiency of the algorithms. For a few representative nonlinear benchmark processes, such as chemical reactor
出版日期Book 2014
關(guān)鍵詞Control; Control Applications; Control Engineering; Mulitlayer Control; Neural Network; Optimization; Pred
版次1
doihttps://doi.org/10.1007/978-3-319-04229-9
isbn_softcover978-3-319-35021-9
isbn_ebook978-3-319-04229-9Series ISSN 2198-4182 Series E-ISSN 2198-4190
issn_series 2198-4182
copyrightSpringer International Publishing Switzerland 2014
The information of publication is updating

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發(fā)表于 2025-3-21 22:48:37 | 只看該作者
MPC Algorithms Based on Double-Layer Perceptron Neural Models: the Prototypes,The “ideal” MPC algorithm with nonlinear optimisation and a few suboptimal MPC algorithms with different on-line linearisation methods are discussed. In order to illustrate properties of the considered MPC algorithms they are compared in two control systems: a yeast fermentation reactor and a high p
板凳
發(fā)表于 2025-3-22 04:20:37 | 只看該作者
地板
發(fā)表于 2025-3-22 05:03:43 | 只看該作者
MPC Algorithms Based on Neural State-Space Models,s well as of two suboptimal MPC-NPL and MPL-NPLPT algorithms are presented. All the algorithms are considered in two versions: with the state set-point trajectory and with the output set-point trajectory. Simulation results are concerned with the polymerisation reactor introduced in the previous cha
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發(fā)表于 2025-3-22 08:44:04 | 只看該作者
MPC Algorithms Based on Neural Multi-Models,hapter is concerned with MPC algorithms based on neural multi-models. The classical dynamic models, both input-output and state-space structures, are used recurrently in MPC algorithms as they calculate the predictions for the whole prediction horizon. In such a case the prediction error is propagat
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發(fā)表于 2025-3-22 13:43:08 | 只看該作者
MPC Algorithms with Neural Approximation,arisation. A specially designed neural network (the neural approximator) approximates on-line the step-response coefficients of the model linearised for the current operating point of the process (such an approach is used in the MPC-NPL-NA and DMC-NA algorithms which are extensions of the MPC-NPL an
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發(fā)表于 2025-3-22 19:38:34 | 只看該作者
Stability and Robustness of MPC Algorithms,ity and robustness are reviewed with a view to using them in the suboptimal MPC algorithms with on-line linearisation. A modification of the dual-mode MPC strategy is thoroughly discussed which leads to the suboptimal MPC algorithm with theoretically guaranteed stability. Finally, a modification of
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發(fā)表于 2025-3-22 21:55:36 | 只看該作者
Cooperation between MPC Algorithms and Set-Point Optimisation Algorithms,first, the classical multi-layer control system structure is discussed, the main disadvantage of which is the necessity of on-line nonlinear optimisation. Three control structures with on-line linearisation for set-point optimisation are presented next: the multi-layer structure with steady-state ta
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