Abstract:Regulation of hypnosis level on bi‐spectral index monitor (BIS) during a surgical procedure in propofol anaesthesia administration is a challenging task for an anaesthesiologist in multi‐tasking environment of the operation theater. Automation in anaesthesia has the potential to solve issues arising from manual administration. Automation in anaesthesia is based on developing the three‐compartmental model including pharmacokinetics and pharmacodynamic of the silico patients. This study focuses on regulation of … Show more
“…The results show the controller's capability to achieve and maintain the desired hypnosis level. The duration of the transient responses (induction phase), 50 s, is lesser than in other studies from the literature [7,9,12].…”
Section: Simulations and Resultscontrasting
confidence: 53%
“…The ranges between 60-70 and 40-60 represent high and moderate hypnotic conditions, respectively. During the surgery, the target value for the BIS is 50 and it is recommended to be kept between 40 and 60 [6][7][8][9].…”
The goals of this paper are: (a) to investigate adaptive and fractional-order adaptive control algorithms for an automatic anesthesia process, using a closed-loop system, and (b) to develop an easy-to-use tool for MATLAB/Simulink to facilitate simulations for users with less knowledge about anesthesia and adaptive control. A model reference adaptive control structure was chosen for the entire system. First of all, to control the patient’s state during the surgery process, the patient mathematical model is useful, or even required for simulation studies. The pharmacokinetic/pharmacodynamics (PK/PD) model was determined using MATLAB’s SimBiology tool, starting from a previously available block diagram, and validated through simulation. Then, to achieve the desired control performances, two controllers are designed: a PI adaptive controller and a PIλ (PI-fractional) adaptive controller, using the MIT algorithm. The time response during anesthetic drug infusion for each patient can be plotted with the AnesthesiaGUIDE tool, which is also designed in MATLAB/Simulink. The tool was tested on data from 12 patients, subjected to general anesthesia, with successful results. Through this tool, the article provides a good opportunity for any user to experience with adaptive control for the anesthesia process.
“…The results show the controller's capability to achieve and maintain the desired hypnosis level. The duration of the transient responses (induction phase), 50 s, is lesser than in other studies from the literature [7,9,12].…”
Section: Simulations and Resultscontrasting
confidence: 53%
“…The ranges between 60-70 and 40-60 represent high and moderate hypnotic conditions, respectively. During the surgery, the target value for the BIS is 50 and it is recommended to be kept between 40 and 60 [6][7][8][9].…”
The goals of this paper are: (a) to investigate adaptive and fractional-order adaptive control algorithms for an automatic anesthesia process, using a closed-loop system, and (b) to develop an easy-to-use tool for MATLAB/Simulink to facilitate simulations for users with less knowledge about anesthesia and adaptive control. A model reference adaptive control structure was chosen for the entire system. First of all, to control the patient’s state during the surgery process, the patient mathematical model is useful, or even required for simulation studies. The pharmacokinetic/pharmacodynamics (PK/PD) model was determined using MATLAB’s SimBiology tool, starting from a previously available block diagram, and validated through simulation. Then, to achieve the desired control performances, two controllers are designed: a PI adaptive controller and a PIλ (PI-fractional) adaptive controller, using the MIT algorithm. The time response during anesthetic drug infusion for each patient can be plotted with the AnesthesiaGUIDE tool, which is also designed in MATLAB/Simulink. The tool was tested on data from 12 patients, subjected to general anesthesia, with successful results. Through this tool, the article provides a good opportunity for any user to experience with adaptive control for the anesthesia process.
“…Sliding mode control (SMC) is proposed to tackle many control problems in engineering applications [39]. There are many variants of SMC such as higher order SMC [39], super-twisting SMC [40], integral SMC [41], robust integral SMC [42], and terminal SMC [43]. In this section, quasi-sliding mode control (QSMC)-based master and slave Rikitake chaotic dynamo systems are proposed to guarantee asymptotic convergence of the synchronization errors of the master-slave Rikitake circuits.…”
Section: Quasi-sliding Mode Control Based Synchronisation Of Rikitake Circuitsmentioning
Rikitake dynamo system (1958) is a famous two-disk dynamo model that is capable of executing nonlinear chaotic oscillations similar to the chaotic oscillations as revealed by palaeomagnetic study. First, we detail the Rikitake dynamo system, its signal plots and important dynamic properties. Then a circuit design using Multisim is carried out for the Rikitake dynamo system. New synchronous quasi-sliding mode control (QSMC) for Rikitake chaotic system is studied in this paper. Furthermore, the selection on switching surface and the existence of QSMC scheme is also designed in this paper. The efficiency of the QSMC scheme is illustrated with MATLAB plots.
“…The controller not only improved the overall efficiency and durability of the PEMFC but also prolonged its stack life. The other applications of SMC include hypnosis regulation in propofol anesthesia 94 and robust control of a serial‐link robotic manipulator 95. Inspite of its well‐known property of being insensitive to mathematical modeling errors, parameter uncertainities, and external disturbances, the chattering effect is a severe drawback which is due to the discontinuous mode of the SMC law.…”
A state‐of‐the‐art review on various identification schemes proposed for the Hammerstein, Wiener, and Volterra systems is presented with respect to the special problems arising in the identification of unknown nonlinear systems. Past and recent developments in the field of nonlinear system identification, parameter estimation, and nonlinear control schemes along with the nonlinearity issues are also deeply investigated. A comprehensive analysis on various parameter estimation approaches and nonlinear control laws are made adding credit to the noteworthy contributions, constructive arguments, and remarkable breakthroughs of researchers in various research fields. The application of most popular nonlinear control strategies for many nonlinear systems in the existing literature are presented spotlighting their merits and major shortcomings. The challenges faced in the nonlinear control field and their emergences to future directions are projected.
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