Robust state estimation for a nonlinear hybrid model of the alternating activated sludge process using filtered high-gain observers
type: Item
instance of: scholarly article
title: Robust state estimation for a nonlinear hybrid model of the alternating activated sludge process using filtered high-gain observers
zbMATH Open document ID: 1459.93127
DOI: 10.1155/2020/8840890
author: Afef Boudagga
author: Habib Dimassi
author: Salim Hadj-Said
author: F. Msahli
published in: Mathematical Problems in Engineering
publication date: 2021-01-04T00:00:00Z
review text: Summary: In this paper, a robust state estimation method based on a filtered high-gain observer is developed for the alternating activated sludge process (AASP) considered as a nonlinear hybrid system. Indeed, we assume that the biodegradable substrate and the ammonia concentrations in the AASP model are unmeasured due to the high cost of their sensors whose maintenance is also very expensive. The observer design is based on the association of the classical high-gain observer and the idea of the application of linear filters on the observation error to deal with measurement noise. It is shown through a Lyapunov analysis that the designed observer ensures the estimation of the unmeasured states (the biodegradable substrate and the ammonia concentrations) based on the measured dissolved oxygen and nitrate concentrations subject to noise. A comparison with the classical high-gain observer is performed via numerical simulations in order to show the robustness of the suggested estimation approach against Gaussian measurement noise.
Mathematics Subject Classification ID: 93C95
Mathematics Subject Classification ID: 93B53
Mathematics Subject Classification ID: 93B07
Mathematics Subject Classification ID: 93C10
zbMATH DE Number: 7290101
MaRDI profile type: Publication
full work available at URL: https://doi.org/10.1155/2020/8840890
OpenAlex ID: W3111722198
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description: scientific article; zbMATH DE number 7290101