Nonlinear predictive control based on neural multi-models

Nonlinear predictive control based on neural multi-models This paper discusses neural multi-models based on Multi Layer Perceptron (MLP) networks and a computationally efficient nonlinear Model Predictive Control (MPC) algorithm which uses such models. Thanks to the nature of the model it calculates...

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Bibliographic Details
Published in:International journal of applied mathematics and computer science 2010-03, Vol.20 (1), p.7
Main Authors: Lawrynczuk, Maciej, Tatjewski, Piotr
Format: Article
Language:English
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Summary:Nonlinear predictive control based on neural multi-models This paper discusses neural multi-models based on Multi Layer Perceptron (MLP) networks and a computationally efficient nonlinear Model Predictive Control (MPC) algorithm which uses such models. Thanks to the nature of the model it calculates 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.
ISSN:1641-876X
2083-8492
DOI:10.2478/v10006-010-0001-y