Filtered model-based predictive control applied to the temperature and humidity control of a neonatal incubator

Cavalcante, Marcos Uchoa; Torrico, Bismark C.; Almeida, Otacílio da Mota; Braga, Arthur P. S.; Filho, Francisco Lincoln Matos da Costa

doi:10.1109/induscon.2010.5739884

Cited by 10 publications

(5 citation statements)

References 10 publications

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“…Although this diagram is quite common [ 25 ], in the same way as transfer function, delay inclusion or exclusion will modify the performance of the PID controller. So, some studies excluded the delay for temperature [ 17 , 22 , 28 ] while others considered it in their schemes for temperature and humidity [ 32 , 36 ]. In our case, an adapted version of Yiping et al [ 28 ] that considers delays has been used, but other complex approaches with predictive algorithms should be considered for future improvements [ 36 ].…”

Section: Discussionmentioning

confidence: 99%

Temperature and Humidity PID Controller for a Bioprinter Atmospheric Enclosure System

et al. 2020

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Bioprinting is a complex process, highly dependent on bioink properties (materials and cells) and environmental conditions (mainly temperature, humidity and CO2 concentration) during the bioprinting process. To guarantee proper cellular viability and an accurate geometry, it is mandatory to control all these factors. Despite internal factors, such as printing pressures, temperatures or speeds, being well-controlled in actual bioprinters, there is a lack in the controlling of external parameters, such as room temperature or humidity. In this sense, the objective of this work is to control the temperature and humidity of a new, atmospheric enclosure system for bioprinting. The control has been carried out with a decoupled proportional integral derivative (PID) controller that was designed, simulated and experimentally tested in order to ensure the proper operation of all its components. Finally, the PID controller can stabilize the atmospheric enclosure system temperature in 311 s and the humidity in 65 s, with an average error of 1.89% and 1.30%, respectively. In this sense, the proposed atmospheric enclosure system can reach and maintain the proper temperature and humidity values during post-printing and provide a pre-incubation environment that promotes stability, integrity and cell viability of the 3D bioprinted structures.

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Section: Discussionmentioning

confidence: 99%

Temperature and Humidity PID Controller for a Bioprinter Atmospheric Enclosure System

et al. 2020

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“…In 2010, the authors of this paper developed a robust predictive control strategy for multivariable systems with multiple delays and input constrains [7].…”

Section: Related Workmentioning

confidence: 99%

Dynamic Matrix Control DMC using the Tuning Procedure based on First Order Plus Dead Time for Infant-Incubator

Ali¹,

Feki²,

Mami³

2019

IJACSA

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The concept of Model Predictive Control (MPC) is considered as one of the most important controlling strategies. It is used in several fields, such as petrochemical, oil refinery, fertilizer and chemical plants. It is also well spread among the clinicians and in the biomedical fields. In this context, our paper aims to investigate the thermal conditions inside the infant incubator for premature babies. In this study, we propose the Dynamic Matrix Control (DMC) as a control strategy. The most particularity of this strategy is applicable to the Multi-input Multi-output (MIMO) systems. It aims to compare different coupled transfer functions achieved by two identification methods in previous work. Also, a simulation of the air temperature and humidity is achieved inside the unit care. In this work, we focus on the tuning controlling parameters because it is considered as a key step in the successful performance of (DMC). Then, to obtain the (DMC), we have used an analytic tool, which is the Process Reaction Curve (PRC), for higher order transfers function because it needs a lot of work for this purpose. It should be approximated as a low order transfer function with time delay, which is achieved by using the First Order Plus Dead Time (FOPDT) of processing models. Finally, the result of the comparison of the infant-incubator is provided to show an optimal and good performance of the thermal behavior of our propos methodology and prove that a good identification ensures a better performance.

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“…Within this period of early life, the incubators are fundamental tools for reducing the risk of mortality and diseases. The incubator provides an adequate microclimate, so that newborn infants can overcome the first occurrences of diseases, and it also controls skin temperature and the relative humidity [ 6 ].…”

Section: Introductionmentioning

confidence: 99%

Assessment and Certification of Neonatal Incubator Sensors through an Inferential Neural Network

Junior

Menezes

Albuquerque

et al. 2013

Sensors

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Measurement and diagnostic systems based on electronic sensors have been increasingly essential in the standardization of hospital equipment. The technical standard IEC (International Electrotechnical Commission) 60601-2-19 establishes requirements for neonatal incubators and specifies the calibration procedure and validation tests for such devices using sensors systems. This paper proposes a new procedure based on an inferential neural network to evaluate and calibrate a neonatal incubator. The proposal presents significant advantages over the standard calibration process, i.e., the number of sensors is drastically reduced, and it runs with the incubator under operation. Since the sensors used in the new calibration process are already installed in the commercial incubator, no additional hardware is necessary; and the calibration necessity can be diagnosed in real time without the presence of technical professionals in the neonatal intensive care unit (NICU). Experimental tests involving the aforementioned calibration system are carried out in a commercial incubator in order to validate the proposal.

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Filtered model-based predictive control applied to the temperature and humidity control of a neonatal incubator

Cited by 10 publications

References 10 publications

Temperature and Humidity PID Controller for a Bioprinter Atmospheric Enclosure System

Temperature and Humidity PID Controller for a Bioprinter Atmospheric Enclosure System

Dynamic Matrix Control DMC using the Tuning Procedure based on First Order Plus Dead Time for Infant-Incubator

Assessment and Certification of Neonatal Incubator Sensors through an Inferential Neural Network

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