TELEMON - A Complex System for Real Time Medical Telemonitoring

Costin, Hariton; Rotariu, Cristian; Alexa, Ioana Dana; Constantinescu, Gina; Cehan, Vlad; Dionisie, Bogdan; Andruseac, Gladiola; Felea, Victor; Crauciuc, Eduard; Scutariu, Monica Mihaela

doi:10.1007/978-3-642-03904-1_25

Cited by 17 publications

(5 citation statements)

References 8 publications

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“…This performance improvement would be certainly different for control systems dedicated to other challenging processes. Such processes include medical applications [78], [79], [80], transportation systems [81], [82], [83], robotics [84], [85], [86], [87], and fault detection [5], [88]. However, the only constraint imposed to these control systems is that they should carry out repetitive tasks.…”

Section: Resultsmentioning

confidence: 99%

Performance Improvement of Low-Cost Iterative Learning-Based Fuzzy Control Systems for Tower Crane Systems

Precup¹,

Roman²,

Hedrea³

et al. 2022

INT J COMPUT COMMUN, Int. J. Comput. Commun. Control

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This paper is dedicated to the memory of Prof. Ioan Dzitac, one of the fathers of this journal and its founding Editor-in-Chief till 2021. The paper addresses the performance improvement of three Single Input-Single Output (SISO) fuzzy control systems that control separately the positions of interest of tower crane systems, namely the cart position, the arm angular position and the payload position. Three separate low-cost SISO fuzzy controllers are employed in terms of first order discrete-time intelligent Proportional-Integral (PI) controllers with Takagi-Sugeno-Kang Proportional-Derivative (PD) fuzzy terms. Iterative Learning Control (ILC) system structures with PD learning functions are involved in the current iteration SISO ILC structures. Optimization problems are defined in order to tune the parameters of the learning functions. The objective functions are defined as the sums of squared control errors, and they are solved in the iteration domain using the recent metaheuristic Slime Mould Algorithm (SMA). The experimental results prove the performance improvement of the SISO control systems after ten iterations of SMA.

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Section: Resultsmentioning

confidence: 99%

Performance Improvement of Low-Cost Iterative Learning-Based Fuzzy Control Systems for Tower Crane Systems

Precup¹,

Roman²,

Hedrea³

et al. 2022

INT J COMPUT COMMUN, Int. J. Comput. Commun. Control

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“…However, the conclusions might be slightly different as far as the performance comparison is concerned if other processes are tackled by model-free controllers; such promising examples including the medical ones are fault diagnosis (Baranyi et al, 2004), real-time medical telemonitoring (Costin et al, 2009), telerobotic systems serving space medicine (Haidegger et al, 2011), hydraulic plants (Angelov et al, 2013), microdrilling operations (Beruvides et al, 2013), smart floors (Hvizdoš et al, 2015), chaotic systems (Precup & Tomescu, 2015), turbojet engines (Andoga & Fozo, 2017), fuzzy systems (Dzitac et al, 2017) and autonomous systems (Deliparaschos et al, 2020).…”

Section: Simulation Results and Comparisonmentioning

confidence: 99%

Model-Free Control of Finger Dynamics in Prosthetic Hand Myoelectric-based Control Systems

Precup¹,

Roman²,

Teban³

et al. 2020

STUD INFORM CONTROL

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“…3 give an indication on the system stability. The regulation and tracking performance of the fuzzy control system were not analyzed; however, the optimal tuning can be carried out in this context, with the results that can be quite different for this application and other challenging ones as well [103][104][105][106][107][108]. Starting with the authors' application of the center manifold theory to Mamdani fuzzy control systems, this paper suggests its application to state feedback Takagi-Sugeno-Kang fuzzy control systems as well.…”

Section: Validation On Electro-hydraulic Servo System Position Controlmentioning

confidence: 99%

A Center Manifold Theory-Based Approach to the Stability Analysis of State Feedback Takagi-Sugeno-Kang Fuzzy Control Systems

Precup

Preitl²,

Petriu³

et al. 2020

FU Mech Eng

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The aim of this paper is to propose a stability analysis approach based on the application of the center manifold theory and applied to state feedback Takagi-Sugeno-Kang fuzzy control systems. The approach is built upon a similar approach developed for Mamdani fuzzy controllers. It starts with a linearized mathematical model of the process that is accepted to belong to the family of single input second-order nonlinear systems which are linear with respect to the control signal. In addition, smooth right-hand terms of the state-space equations that model the processes are assumed. The paper includes the validation of the approach by application to stable state feedback Takagi-Sugeno-Kang fuzzy control system for the position control of an electro-hydraulic servo-system.

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TELEMON - A Complex System for Real Time Medical Telemonitoring

Cited by 17 publications

References 8 publications

Performance Improvement of Low-Cost Iterative Learning-Based Fuzzy Control Systems for Tower Crane Systems

Performance Improvement of Low-Cost Iterative Learning-Based Fuzzy Control Systems for Tower Crane Systems

Model-Free Control of Finger Dynamics in Prosthetic Hand Myoelectric-based Control Systems

A Center Manifold Theory-Based Approach to the Stability Analysis of State Feedback Takagi-Sugeno-Kang Fuzzy Control Systems

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