Industrial Control under Non-Ideal Measurements : Data-Based Signal Processing as an Alternative to Controller Retuning
Pisa, Ivan (Universitat Autònoma de Barcelona. Departament de Telecomunicació i Enginyeria de Sistemes)
Morell Pérez, Antoni (Universitat Autònoma de Barcelona. Departament de Telecomunicació i Enginyeria de Sistemes)
Vilanova i Arbós, Ramón (Universitat Autònoma de Barcelona. Departament de Telecomunicació i Enginyeria de Sistemes)
López Vicario, José (Universitat Autònoma de Barcelona. Departament de Telecomunicació i Enginyeria de Sistemes)

Date:	2021
Abstract:	Industrial environments are characterised by the non-lineal and highly complex processes they perform. Different control strategies are considered to assure that these processes are correctly performed. Nevertheless, these strategies are sensible to noise-corrupted and delayed measurements. For that reason, denoising techniques and delay correction methodologies should be considered but, most of these techniques require a complex design and optimisation process as a function of the scenario where they are applied. To alleviate this, a complete data-based approach devoted to denoising and correcting the delay of measurements is proposed here with a two-fold objective: simplify the solution design process and achieve its decoupling from the considered control strategy as well as from the scenario. Here it corresponds to a Wastewater Treatment Plant (WWTP). However, the proposed solution can be adopted at any industrial environment since neither an optimization nor a design focused on the scenario is required, only pairs of input and output data. Results show that a minimum Root Mean Squared Error (RMSE) improvement of a 63. 87% is achieved when the new proposed data-based denoising approach is considered. In addition, the whole system performance show that similar and even better results are obtained when compared to scenario-optimised methodologies.
Grants:	Agència de Gestió d'Ajuts Universitaris i de Recerca 2017/SGR-1202 Agència de Gestió d'Ajuts Universitaris i de Recerca 2017/SGR-1670 Agència de Gestió d'Ajuts Universitaris i de Recerca 2020/FI_B2-00038 Agencia Estatal de Investigación TEC2017-84321-C4-4-R Agencia Estatal de Investigación DPI2016-77271-R
Rights:	Aquest document està subjecte a una llicència d'ús Creative Commons. Es permet la reproducció total o parcial, la distribució, la comunicació pública de l'obra i la creació d'obres derivades, fins i tot amb finalitats comercials, sempre i quan es reconegui l'autoria de l'obra original.
Language:	Anglès
Document:	Article ; recerca ; Versió publicada
Subject:	Artificial neural networks ; Data-driven methods ; Denoising autoencoders ; Industrial control ; Wastewater treatment plants
Published in:	Sensors (Basel, Switzerland), Vol. 21, Núm. 4 (February, 2021) , art. 1237, ISSN 1424-8220

DOI: 10.3390/s21041237
PMID: 33578649

31 p, 2.2 MB

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