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2007 | 17 | 2 | 217-232
Tytuł artykułu

A family of model predictive control algorithms with artificial neural networks

Treść / Zawartość
Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
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.
Rocznik
Tom
17
Numer
2
Strony
217-232
Opis fizyczny
Daty
wydano
2007
otrzymano
2006-12-13
(nieznana)
2006-12-15
poprawiono
2007-04-18
Twórcy
  • Institute of Control and Computation Engineering, Faculty of Electronics and Information Technology, Warsaw University of Technology, ul. Nowowiejska 15/19, 00–665 Warsaw, Poland
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Typ dokumentu
Bibliografia
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