Industrial Control under Non-Ideal Measurements: Data-Based Signal Processing as an Alternative to Controller Retuning

Sensors

Ramón Vilanova¹  Antoni Morell²  Ivan Pisa²  JoseLopez Vicario² 

[1] Advanced Systems for Automation and Control (ASAC) Group, Escola d’Enginyeria, Universitat Autònoma de Barcelona, 08193 Bellaterra, Spain;Wireless Information Networking (WIN) Group, Escola d’Enginyeria, Universitat Autònoma de Barcelona, 08193 Bellaterra, Spain;

关键词: artificial neural networks;    data-driven methods;    denoising autoencoders;    industrial control;    wastewater treatment plants;   

DOI  :  10.3390/s21041237

来源: DOAJ

【 摘 要 】

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.

【 授权许可】

Unknown