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Biblioteca Julio Casti馿iras. Sistema de Informaci髇 Integrado - Facultad de Ingenier韆 UNLP
Facultad de Ingenier韆 | 115 esq.47 | Horario: Lunes a Viernes 8 a 19 hs.
E-mail: bibcentral@ing.unlp.edu.ar
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Información bibliografica (registro INGC-EBK-000389) |
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T韙ulo: |
Computationally Efficient Model Predictive Control Algorithms A Neural Network Approach / by Maciej Lawrynczuk. |
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Autor:
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Lawrynczuk, Maciej.
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Editado por: |
Springer International Publishing :;Imprint: Springer,
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Año de publicación: |
2014.
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Lugar de publicación: |
Cham :
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Descripción física: |
xxiv, 316 p. : il. |
ISBN: |
9783319042299
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Colecci髇: |
Studies in Systems, Decision and Control,
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Materias: |
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Artificial Intelligence (incl. Robotics). |
Control. |
Control engineering. |
Computational intelligence. |
Engineering. |
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Notas: |
MPC Algorithms -- MPC Algorithms Based on Double-Layer Perceptron Neural Models: the Prototypes -- MPC Algorithms Based on Neural Hammerstein and Wiener Models -- MPC Algorithms Based on Neural State-Space Models -- MPC Algorithms Based on Neural Multi-Models -- MPC Algorithms with Neural Approximation -- Stability and Robustness of MPC Algorithms -- Cooperation Between MPC Algorithms and Set-Point Optimisation Algorithms. |
Sumario: |
This book thoroughly discusses computationally efficient (suboptimal) Model Predictive Control (MPC) techniques based on neural models. The subjects treated include: 路聽聽聽聽聽聽聽聽 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. 路聽聽聽聽聽聽聽聽 Implementation details of the MPC algorithms for feedforward perceptron neural models, neural Hammerstein models, neural Wiener models and state-space neural models. 路聽聽聽聽聽聽聽聽 The MPC algorithms based on neural multi-models (inspired by the idea of predictive control). 路聽聽聽聽聽聽聽聽 The MPC algorithms with neural approximation with no on-line linearization. 路聽聽聽聽聽聽聽聽 The MPC algorithms with guaranteed stability and robustness. 路聽聽聽聽聽聽聽聽 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 reactors and a distillation column, for which the classical MPC algorithms based on linear models do not work properly, the trajectories obtained in the suboptimal MPC algorithms are very similar to those given by the ``ideal'' MPC algorithm with on-line nonlinear optimization repeated at each sampling instant. At the same time, the suboptimal MPC algorithms are significantly less computationally demanding. |
URL: |
http://dx.doi.org/10.1007/978-3-319-04229-9
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Tapa y contenido (Amazon.com) |
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