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“…For example, in medical CPS, respiratory modules are used to assist patients to breathe. These modules are capable of tracking time-varying target pressure, estimating the hose characteristics of the module, and adjusting the pressure over the hose according to the need of the patient [94]. In smart agricultural and autonomous robotic systems, applications of adaptive control can be found in irrigation and moisture distribution [95] and military surveillance and intervention [96] respectively.…”
With the emergence of the Internet-of-Things (IoT), artificial intelligence, and communication technologies, cyber-physical systems (CPS) have revolutionized the engineering paradigm with profound applications in many aspects of society including homes, energy, agriculture, health-care, transportation, business, and manufacturing. A CPS uses suitable computational techniques such as game theory to enable different entities to interact with one another for taking necessary actions to obtain selected objectives. Recent literature on CPS has extensively used game theory to approach a variety of technical challenges. In order to make these contributions more accessible to a broader audience, there is a need for studies that can provide readers with a comprehensive understanding of different types of CPS and their attributes, then clearly outline why game theory is relevant for modeling different aspects of CPS, and also discuss how game theory has been used in relevant literature to date. This paper bridges this gap by 1) providing a general discussion of different types of CPS and their characteristics; 2) giving an overview of different types of game-theoretic approaches; 3) explaining why game theory is appropriate for modeling different types of CPS; and 4) finally, studying how game theory has been used in different CPS types to address their challenges. Further, we also identify some key research challenges for future investigation where game theory could be applied as a potential solution.
“…For example, in medical CPS, respiratory modules are used to assist patients to breathe. These modules are capable of tracking time-varying target pressure, estimating the hose characteristics of the module, and adjusting the pressure over the hose according to the need of the patient [94]. In smart agricultural and autonomous robotic systems, applications of adaptive control can be found in irrigation and moisture distribution [95] and military surveillance and intervention [96] respectively.…”
With the emergence of the Internet-of-Things (IoT), artificial intelligence, and communication technologies, cyber-physical systems (CPS) have revolutionized the engineering paradigm with profound applications in many aspects of society including homes, energy, agriculture, health-care, transportation, business, and manufacturing. A CPS uses suitable computational techniques such as game theory to enable different entities to interact with one another for taking necessary actions to obtain selected objectives. Recent literature on CPS has extensively used game theory to approach a variety of technical challenges. In order to make these contributions more accessible to a broader audience, there is a need for studies that can provide readers with a comprehensive understanding of different types of CPS and their attributes, then clearly outline why game theory is relevant for modeling different aspects of CPS, and also discuss how game theory has been used in relevant literature to date. This paper bridges this gap by 1) providing a general discussion of different types of CPS and their characteristics; 2) giving an overview of different types of game-theoretic approaches; 3) explaining why game theory is appropriate for modeling different types of CPS; and 4) finally, studying how game theory has been used in different CPS types to address their challenges. Further, we also identify some key research challenges for future investigation where game theory could be applied as a potential solution.
“…In the simulations, a single-hose ventilation setup is considered, as in [40]. A single-hose ventilation system is considered because it simplifies modeling, since no expiration valve is used.…”
Mechanical ventilators facilitate breathing for patients who cannot breathe (sufficiently) on their own. The aim of this paper is to estimate relevant lung parameters and the spontaneous breathing effort of a ventilated patient that help keeping track of the patient's clinical condition. A key challenge is that estimation using the available sensors for typical model structures results in a non-identifiable parametrization. A sparse optimization algorithm to estimate the lung parameters and the patient effort, without interfering with the patient's treatment, using an 1 -regularization approach is presented. It is confirmed that accurate estimates of the lung parameters and the patient effort can be retrieved through a simulation case study and an experimental case study.
“…The dynamics of the lung can be represented by the following equation [13] where is the equivalent capacitance of the lung, is the inflow, is a coefficient related to its time constant, and is a disturbance exerted by the patient’s muscles, which is considered limited in amplitude and its derivative. Neglecting the dynamics of the air ducts, it can be considered that and , so the dynamics of the lung are given by Fig.…”
We propose a mandatory invasive mechanical ventilator prototype for severe COVID-19 patients with volume and pressure control operation modes. This system comprises basic pneumatic elements and sensors. Its performance is similar to commercial equipment, and it presents robustness to external disturbances and parametric uncertainties. To develop a control strategy, we propose a mathematical model with a variable structure that incorporates the dead zone phenomenon of the proportional valve, and considers external disturbances and parametric uncertainties. Based on this model, we propose a global control strategy that is based on pressure and flow regulation controllers, which use the active disturbances rejection control structure (ADRC). In this strategy, we propose robust state observers to estimate disturbances and the signals necessary for implementing the controllers. We illustrate the performance of the prototype and the control strategy through numerical simulations and experiments. We also compare its performance with PID controllers. These results corroborate its effectiveness and the possibility of its application in invasive mechanical ventilators with a simple structure, which can significantly help critical care of COVID-19 inpatients.
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