A polynomial design approach to robust control of neuromuscular blockade of patients subject to general anesthesia

Caiado, Daniela V.; Lemos, João M.; Costa, Bertinho A.; Paz, Luis Alberto Tavares-de la; Mendonça, Teresa

doi:10.1109/cdc.2013.6759871

Cited by 3 publications

(3 citation statements)

References 23 publications

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“…This type of design is in the scope of robust control, of which [24] and [25] are early attempts. Although, in the case of single-input, single-output plants, robust pole placement controllers can be designed using a trial-and-error method [26], it is advantageous to use a systematic procedure such as the H3 method [27]. An alternative approach would be to design a fixed-parameter controller by applying the techniques described in [22].…”

Section: » a P P L I C At I O N S O F C O N T R O Lmentioning

confidence: 99%

Robust Control of Maintenance-Phase Anesthesia [Applications of Control]

2014

IEEE Control Syst.

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Section: » a P P L I C At I O N S O F C O N T R O Lmentioning

confidence: 99%

Robust Control of Maintenance-Phase Anesthesia [Applications of Control]

2014

IEEE Control Syst.

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“…The models obtained from the identification process are used to expand the normalized (GALENO) database and to improve the characterization the patient population. This is used in computer simulation to development and to evaluate new control algorithms that will be available in the platform 11‐13 for simulation and training and for real‐time control 14 . To facilitate the rapid development of the platform and the implementation of new control algorithms, the MatLab software is used, but due to the high cost of the MatLab software, it may be seen as a constraint.…”

Section: Introductionmentioning

confidence: 99%

GALENO: Computer aided system for modeling, monitoring, and control in anesthesia

Costa

Mendonça

2021

Adv Control Appl

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This article presents a real‐time software platform (the GALENO platform) that was designed to support the development of algorithms to automate anesthesia procedures. Automation of anesthesia can contribute to improve the anesthesia practice, by personalizing health care, and by allowing the anesthesiologist to concentrate on supervisory tasks, avoiding repetitive manual work that can be executed and optimized by using automatic control systems. The GALENO platform is used for rapid prototyping of new control algorithms applied to anesthesia and was developed to be used by medical staff as a supporting tool for general anesthesia in a clinical setting. It has been tested in simulation and in real clinical settings. The platform can control the administration of several anesthetic drug combinations for total intravenous anesthesia using the DoA/BIS index and the NMB level, during induction and maintenance phases. To highlight some of the main features of the platform, and its practical results, two control algorithms are used as examples, the closed‐loop predictive min–max target controlled infusion is described and is used to control DoA/BIS, and a self‐tuning proportional‐integral‐derivative is used to implement the control of the NMB.

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“…In which the design of controller is built in a way that it can modify all cases not only nominal one, but all the cases will differ by a suitable range, caused by known values (robust stability). It can work perfectly, but must guarantee a minimum competence in set point tracking and robustness to disturbance (robust performance) [3].…”

Section: Introductionmentioning

confidence: 99%

Design of adaptive neuro sliding mode controller for anesthesia drug delivery based on biogeography based optimization

Abood¹,

Karam²,

Issa³

2019

IJACR

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Monitoring depth of anesthesia (DOA) is a significant point in general anesthesia (GA). It can be obtained from the assessment of the drug dose carefully and preciously. As a benefit of drug delivery automation, closed-loop method will present several advantages. It may prevent excessive dose amount or less needed dose and the controlled feedback system can decrease the cost of the healthcare by reducing the patient recovery period. This paper addresses the use of adaptive sliding mode controllers (ASMC) for calculating the depth of anesthesia by administrating a dose of propofol drug and measure patient state according to the monitoring device the bispectral index (BIS). In this study, we suggest a simple nonlinear control strategy consists of an ASMC combined with single neuron self-tune neural controller. It is used for maintaining DOA and reducing the effect of the nonlinear element. The adaptive controller uses the BIS value measured as a reference tracking value and propofol dose rate as a control signal. The parameters of the controller are tuned using a procedure based on the biogeography-based optimization (BBO) algorithm. The results indicate that including adaptive parts of the controller and tune its gains needed by BBO algorithm may enable optimal and stable performance for controller for all patients. It also provides fast reach to the induction phase and stay in a stable value in maintenance phase, which reflects the efficient response of the suggested controller if it compared to other nonlinear controllers. It is also justified by the results obtained that the suggested controller gives a very good response.

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A polynomial design approach to robust control of neuromuscular blockade of patients subject to general anesthesia

Cited by 3 publications

References 23 publications

Robust Control of Maintenance-Phase Anesthesia [Applications of Control]

Robust Control of Maintenance-Phase Anesthesia [Applications of Control]

GALENO: Computer aided system for modeling, monitoring, and control in anesthesia

Design of adaptive neuro sliding mode controller for anesthesia drug delivery based on biogeography based optimization

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