A FAMILY OF MODEL PREDICTIVE CONTROL ALGORITHMS WITH ARTIFICIAL NEURAL NETWORKS.
In: International Journal of Applied Mathematics & Computer Science, Jg. 17 (2007-06-01), Heft 2, S. 217-232
Online
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Zugriff:
This paper details nonlinear Model-based Predictive Control (MPC) algorithms for MIMO processes modelled by means of neural networks of a feedforward structure. Two general MPC techniques are considered: the one with Nonlinear Optimisation (MPC-NO) and the one with Nonlinear Prediction and Linearisation (MPC-NPL). In the first case a nonlinear optimisation problem is solved in real time on-line. In order to reduce the computational burden, in the second case a neural model of the process is used on-line to determine local linearisation and a nonlinear free trajectory. Single-point and multi-point linearisation methods are discussed. The MPC-NPL structure is far more reliable and less computationally demanding in comparison with the MPC-NO one because it solves a quadratic programming problem, which can be done efficiently within a foreseeable time frame. At the same time, closed-loop performance of both algorithm classes is similar. Finally, a hybrid MPC algorithm with Nonlinear Prediction, Linearisation and Nonlinear optimisation (MPC-NPL-NO) is discussed. [ABSTRACT FROM AUTHOR]
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Titel: |
A FAMILY OF MODEL PREDICTIVE CONTROL ALGORITHMS WITH ARTIFICIAL NEURAL NETWORKS.
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Autor/in / Beteiligte Person: | ławryńczuk, Maciej |
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Zeitschrift: | International Journal of Applied Mathematics & Computer Science, Jg. 17 (2007-06-01), Heft 2, S. 217-232 |
Veröffentlichung: | 2007 |
Medientyp: | academicJournal |
ISSN: | 1641-876X (print) |
DOI: | 10.2478/v10006-007-0020-5 |
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