Literature DB >> 34372393

Adaptive Pressure Control System Based on the Maximum Correntropy Criterion.

Thommas Kevin Sales Flores1, Juan Moises Mauricio Villanueva1, Heber Pimentel Gomes2, Sebastian Yuri Cavalcanti Catunda3.   

Abstract

Water supply systems are constantly improving their operation through energy efficiency actions that involve the use of advanced measurement, control, and automation techniques. The maintenance and reliability of water distribution is directly associated with hydraulic pressure control. The main challenges encountered in hydraulic pressure control are associated with random changes in the supply plant and the presence of noise and outliers in the sensor measurements. These undesired characteristics cause inefficiency and instability in the control system of the pumping stations. In this scenario, this paper proposes an indirect adaptive control methodology by reference model for modeling and controlling water supply systems. The criterion adopted in the parametric estimation mechanism and the controller adaptation is the Maximum Correntropy. Experimental results obtained with an experimental bench plant showed that the maximum tracking error was 15% during demand variation, percentage overshoot less than 5%, and steady-state error less than 2%, and the control system became robust to noise and outliers. In comparison to the Mean Squared Error criterion, when noise and outliers influence the sensor signal, the proposed methodology stands out, reducing the mean error and the standard deviation, in the worst-case scenario, by more than 1500%. The proposed methodology, therefore, allows for increased reliability and efficiency of an advanced pump control system, avoiding downtime and equipment damage.

Entities:  

Keywords:  adaptive system; correntropy; water pumping

Year:  2021        PMID: 34372393     DOI: 10.3390/s21155156

Source DB:  PubMed          Journal:  Sensors (Basel)        ISSN: 1424-8220            Impact factor:   3.576


  1 in total

1.  Development of a Soft Sensor for Flow Estimation in Water Supply Systems Using Artificial Neural Networks.

Authors:  Robson Pacífico Guimarães Lima; Juan Moises Mauricio Villanueva; Heber Pimentel Gomes; Thommas Kevin Sales Flores
Journal:  Sensors (Basel)       Date:  2022-04-18       Impact factor: 3.576

  1 in total

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