Implantable Pacemakers Control and Optimization via Fractional Calculus Approaches: A Cyber-Physical Systems Perspective

Bogdan, Paul; Jain, Siddharth; Goyal, Kartikeya; Mărculescu, Radu

doi:10.1109/iccps.2012.11

Cited by 30 publications

(19 citation statements)

References 38 publications

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“…Bogdan et al has proved that the FFLM is capable of modeling traditional CPS [16]. However, because chaotic systems are extremely sensitive to even tiny disturbance signals, hence the feedback signals in this model would possibly lead to an irredeemable disaster: the systems' dynamic evolutions deviate far from the expected trajectory, which means this traditional model is impossible to stable CPS when they are chaotic.…”

Section: Utilization Of Fuzzy Feedback Linearization Modelsmentioning

confidence: 95%

“…This is because it may include unknown or unpredictable variables. Zhang et al developed a hybrid neural network and fuzzy control for automatic generation control in power systems [16]. In their work, recurrent neural network is employed to forecast control and future output of the system based on the current area control error (ACE) and the predicted change-of-ACE.…”

Section: Related Workmentioning

confidence: 99%

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A big data inspired chaotic solution for fuzzy feedback linearization model in cyber-physical systems

et al. 2015

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Cyber-Physical System (CPS) that integrated computational and physical capabilities has emerged as a promising topic. It interacts with physical world and humans through ad hoc communications. In contemporary, with the fast development of CPS theory and applications, CPS generates a large volume of data, which may lead its control into a chaotic status. Hence, it is an impressing demand to solve the chaotic status CPS control. To this end, chaotic time series prediction algorithm are employed to resolve the chaotic status featured by a fuzzy feedback linearization model. Modelling the CPS under big data without taking chaotic features into account may result in an unexpected results. This is because chaotic CPS is dramatically sensitive to small disturbances or minor changes of initiators. This paper developed a CPS model in light of fuzzy feedback linearization. Further, the chaotic time prediction algorithm is applied to solve the chaotic control problem in CPS. The developed algorithm takes both tracking control problem and synchronization control problem into account. The numerical results suggest that the developed method is feasible and efficient in tracking control problem and synchronize two chaotic CPS.

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Section: Utilization Of Fuzzy Feedback Linearization Modelsmentioning

confidence: 95%

Section: Related Workmentioning

confidence: 99%

A big data inspired chaotic solution for fuzzy feedback linearization model in cyber-physical systems

et al. 2015

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“…There must be a unified definition and representation of these events. A certain systematic mechanism must be implemented to compose these CPS events from the higher and lower levels and across the different system boundaries [74]. The event model that results from this composition can both serve as an offline analysis and run-time implementation.…”

Section: Event-based Cps Modelsmentioning

confidence: 99%

Robust Cyber–Physical Systems: Concept, models, and implementation

Lü

Vasilakos

et al. 2016

Future Generation Computer Systems

148

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In this paper we comprehensively survey the concept and strategies for building a resilient and integrated cyber-physical system (CPS). Here resilience refers to a 3S-oriented design, that is, stability, security, and systematicness: Stability means the CPS can achieve a stable sensing-actuation close-loop control even though the inputs (sensing data) have noise or attacks; Security means that the system can overcome the cyber-physical interaction attacks; and Systematicness means that the system has a seamless integration of sensors and actuators. We will also explain the CPS modeling issues since they serve as the basics of 3S design. We will use two detailed examples from our achieved projects to explain how to achieve a robust, systematic CPS design: Case study 1 is on the design of a rehabilitation system with cyber (sensors) and physical (robots) integration. Case Study 2 is on the implantable medical device design. It illustrates the nature of CPS security principle. The dominant feature of this survey is that it has both principle discussions and practical cyber-physical coupling design. 1

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“…Finally, data-driven models such as [7] fit (fractional) differential equations directly to measured heart rate without modeling the underlying mechanisms, and are used for optimal control.…”

Section: Fig 3 Timing Of Electrical Behaviors Of the Heart Are Modementioning

confidence: 99%

The Challenges of High-Confidence Medical Device Software

et al. 2016

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Implantable Pacemakers Control and Optimization via Fractional Calculus Approaches: A Cyber-Physical Systems Perspective

Cited by 30 publications

References 38 publications

A big data inspired chaotic solution for fuzzy feedback linearization model in cyber-physical systems

A big data inspired chaotic solution for fuzzy feedback linearization model in cyber-physical systems

Robust Cyber–Physical Systems: Concept, models, and implementation

The Challenges of High-Confidence Medical Device Software

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