Nonlinear model-predictive control based on quasi-ARX radial-basis function-neural-network

Citation

Sutrisno, Imam and Abu Jami’in, Mohammad and Hu, Jinglu and Marhaban, Mohammad Hamiruce and Mariun, Norman (2014) Nonlinear model-predictive control based on quasi-ARX radial-basis function-neural-network. In: 8th Asia Modelling Symposium (AMS 2014), 23-25 Sept. 2014 , Taipei, Taiwan. (pp. 104-109).

Abstract

A nonlinear model-predictive control (NMPC) is demonstrated for nonlinear systems using an improved fuzzy switching law. The proposed moving average filter fuzzy switching law (MAFFSL) is composed of a quasi-ARX radial basis function neural network (RBFNN) prediction model and a fuzzy switching law. An adaptive controller is designed based on a NMPC. a MAFFSL is constructed based on the system switching criterion function which is better than the (ON/OFF) switching law and a RBFNN is used to replace the neural network (NN) in the quasi-ARX black box model which is understood in terms of parameters and is not an absolute black box model, in comparison with NN. The proposed controller performance is verified through numerical simulations to demonstrate the effectiveness of the proposed method.

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Additional Metadata

Item Type:	Conference or Workshop Item (Paper)
Divisions:	Centre for Advanced Power and Energy Research Faculty of Engineering
DOI Number:	https://doi.org/10.1109/AMS.2014.30
Publisher:	IEEE
Keywords:	Nonlinear model-predictive control; Moving average filter fuzzy switching law; Quasi-ARX radial basis function neural network
Depositing User:	Azian Edawati Zakaria
Date Deposited:	03 Dec 2015 08:52
Last Modified:	28 Jan 2016 02:52
Altmetrics:	http://www.altmetric.com/details.php?domain=psasir.upm.edu.my&doi= 10.1109/AMS.2014.30
URI:	http://psasir.upm.edu.my/id/eprint/41489
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