Industrial control under non-ideal measurements: Data-based signal processing as an alternative to controller retuning

Ivan Pisa*, Antoni Morell, Ramón Vilanova, Jose Lopez Vicario

*Corresponding author for this work

Research output: Contribution to journalArticleResearchpeer-review

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.

Original languageEnglish
Article number1237
Pages (from-to)1-31
Number of pages31
JournalSensors (Switzerland)
Volume21
Issue number4
DOIs
Publication statusPublished - 2 Feb 2021

Keywords

  • Artificial neural networks
  • Data-driven methods
  • Denoising autoencoders
  • Industrial control
  • Wastewater treatment plants

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