Autor:

Ławryńczuk, Maciej ; Tatjewski, Piotr

Współtwórca:

Korbicz, Józef (1951- ) - red. ; Uciński, Dariusz - red.

Tytuł:

Nonlinear predictive control based on neural multi-models

Tytuł publikacji grupowej:

AMCS, Volume 20 (2010)

Temat i słowa kluczowe:

process control ; model predictive control ; neural networks ; optimisation ; linearisation

Abstract:

This paper discusses neural multi-models based on Multi Layer Perceptron (MLP) networks and a computationally efficientnonlinear Model Predictive Control (MPC) algorithm which uses such models. Thanks to the nature of the model itcalculates future predictions without using previous predictions. This means that, unlike the classical Nonlinear Auto Regressive with eXternal input (NARX) model, the multi-model is not used recurrently in MPC, and the prediction error is not propagated. In order to avoid nonlinear optimisation, in the discussed suboptimal MPC algorithm the neural multi-model is linearised on-line and, as a result, the future control policy is found by solving of a quadratic programming problem.

Wydawca:

Zielona Góra: Uniwersytet Zielonogórski

Data wydania:

2010

Typ zasobu:

artykuł

DOI:

10.2478/v10006-010-0001-y

Strony:

7-21

Źródło:

AMCS, Volume 20, Number 1 (2010) ; kliknij tutaj, żeby przejść

Jezyk:

eng

Prawa do dysponowania publikacją:

Biblioteka Uniwersytetu Zielonogórskiego