<abstract xml:lang="en" xmlns="http://www.w3.org/1999/xhtml"><p>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.</p></abstract>

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.

Nonlinear predictive control based on neural multi-models

Institute of Control and Computation Engineering, Warsaw University of Technology, ul. Nowowiejska 15/19, 00-665 Warsaw, Poland

International Journal of Applied Mathematics and Computer Science

{"article-title":"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...

Nonlinear predictive control based on neural multi-models

Online veröffentlicht: 25. März 2010

Seitenbereich: 7 - 21

DOI: https://doi.org/10.2478/v10006-010-0001-y

Schlüsselwörter
process control, model predictive control, neural networks, optimisation, linearisation

This content is open access.

Nonlinear predictive control based on neural multi-models

Online veröffentlicht: 25. März 2010

Seitenbereich: 7 - 21

DOI: https://doi.org/10.2478/v10006-010-0001-y

Schlüsselwörterprocess control, model predictive control, neural networks, optimisation, linearisation

This content is open access.

Schlüsselwörter
process control, model predictive control, neural networks, optimisation, linearisation