1,904 research outputs found
Robust fractional order PI control for cardiac output stabilisation
Drug regulatory paradigms are dependent on the hemodynamic system as it serves to distribute and clear the drug in/from the body. While focusing on the objective of the drug paradigm at hand, it is important to maintain stable hemodynamic variables. In this work, a biomedical application requiring robust control properties has been used to illustrate the potential of an autotuning method, referred to as the fractional order robust autotuner. The method is an extension of a previously presented autotuning principle and produces controllers which are robust to system gain variations. The feature of automatic tuning of controller parameters can be of great use for data-driven adaptation during intra-patient variability conditions. Fractional order PI/PD controllers are generalizations of the well-known PI/PD controllers that exhibit an extra parameter usually used to enhance the robustness of the closed loop system. (C) 2019, IFAC (International Federation of Automatic Control) Hosting by Elsevier Ltd. All rights reserved
Depth of anesthesia control using internal model control techniques
The major difficulty in the design of closed-loop control during anaesthesia is the inherent patient variability due to differences in demographic and drug tolerance. These
discrepancies are translated into the pharmacokinetics (PK),
and pharmacodynamics (PD). These uncertainties may affect
the stability of the closed loop control system. This paper aims at developing predictive controllers using Internal Model Control technique. This study develops patient dose-response models and to provide an adequate drug administration regimen for the anaesthesia to avoid under or over dosing of the patients. The controllers are designed to compensate for patients inherent drug response variability, to achieve the best output disturbance rejection, and to maintain optimal set point response. The results are evaluated compared with traditional PID controller and the performance is confirmed in our
simulation
Fractional order impedance models as rising tools for quantification of unconscious analgesia
This research focuses on modeling the diffusion process that occurs in the human body when an analgesic drug is taken up, by using fractional-order impedance models (FOIMs). We discuss the measurement of a suitable feedback signal that can be used in a model-based control strategy. With this knowledge an early dawn concept of a pain sensor is presented. The major challenges that are encountered during this development consist of identification of the patient model, validation of the pain sensor and validation of the effect of the analgesic drug
Controlo de sistemas compartimentais com incertezas
Doutoramento em MatemáticaOs sistemas compartimentais são frequentemente usados na modelação de
diversos processos em várias áreas, tais como a biomedicina, ecologia,
farmacocinética, entre outras.
Na maioria das aplicações práticas, nomeadamente, aquelas que dizem
respeito à administração de drogas a pacientes sujeitos a cirurgia, por
exemplo, a presença de incertezas nos parâmetros do sistema ou no estado
do sistema é muito comum. Ao longo dos últimos anos, a análise de sistemas
compartimentais tem sido bastante desenvolvida na literatura. No entanto, a
análise da sensibilidade da estabilidade destes sistemas na presença de
incertezas tem recebido muito menos atenção.
Nesta tese, consideramos uma lei de controlo por realimentação do estado
com restrições de positividade e analisamos a sua robustez quando aplicada a
sistemas compartimentais lineares e invariantes no tempo com incertezas nos
parâmetros. Além disso, para sistemas lineares e invariantes no tempo com
estado inicial desconhecido, combinamos esta lei de controlo com um
observador do estado e a robustez da lei de controlo resultante também é
analisada.
O controlo do bloqueio neuromuscular por meio da infusão contínua de um
relaxante muscular pode ser modelado como um sistema compartimental de
três compartimentos e tem sido objecto de estudo por diversos grupos de
investigação. Nesta tese, os nossos resultados são aplicados a este problema
de controlo e são fornecidas estratégias para melhorar os resultados obtidos.Compartmental systems are widely used for modeling several processes in
many fields such as biomedicine, ecology, pharmacokinetics, among others.
In most practical applications, as for instance those concerning drug
administration to patients undergoing surgery, the presence of uncertainties in
the system parameters or in the system state is very common. Over the last
several years the analysis of compartmental systems has been widely
developed in the literature. However, the analysis of the sensitivity of the
stability of these systems under the presence of uncertainties has received far
less attention.
In this thesis, we consider a state feedback control law with positivity
constraints and analyze its robustness when applied to linear time-invariant
compartmental systems with parameter uncertainties. Moreover, for linear timeinvariant
compartmental systems with unknown initial state, we combine this
control law with a state-observer and the robustness of the resulting control law
is also analyzed.
The control of the neuromuscular blockade by the continuous infusion of a
muscle relaxant may be modelled as a three-compartment system and has
been a subject of study by several research groups. In this thesis, our results
are applied to this control problem and strategies for improving the obtained
results are provided.FCT; POPH/FS
Patient specific model based induction of hypnosis using fractional order control
Optimal and safe control of drug delivery systems with continuous infusion protocol is of key importance to avoid over- or under-dosing of the patient. By implementing close-loops one is able to optimize the amount of drug given to the patient. In this paper a robust control methodology is presented. Emerging tools from fractional calculus have been considered and a fractional order PI controller for drug dosing during hypnosis has been designed. In this paper a robust fractional order control of hypnosis is proposed. The controller has been evaluated on an artificial data set of 24 patients and the results indicate that such a control strategy is robust to uncertainty stemming from the inter- and intra-patient variability. (C) 2017, IFAC (International Federation of Automatic Control) Hosting by Elsevier Ltd. All rights reserved
An open source patient simulator for design and evaluation of computer based multiple drug dosing control for anesthetic and hemodynamic variables
We are witnessing a notable rise in the translational use of information technology and control systems engineering tools in clinical practice. This paper empowers the computer based drug dosing optimization of general anesthesia management by means of multiple variables for patient state stabilization. The patient simulator platform is designed through an interdisciplinary combination of medical, clinical practice and systems engineering expertise gathered in the last decades by our team. The result is an open source patient simulator in Matlab/Simulink from Mathworks(R). Simulator features include complex synergic and antagonistic interaction aspects between general anesthesia and hemodynamic stabilization variables. The anesthetic system includes the hypnosis, analgesia and neuromuscular blockade states, while the hemodynamic system includes the cardiac output and mean arterial pressure. Nociceptor stimulation is also described and acts as a disturbance together with predefined surgery profiles from a translation into signal form of most commonly encountered events in clinical practice. A broad population set of pharmacokinetic and pharmacodynamic (PKPD) variables are available for the user to describe both intra- and inter-patient variability. This simulator has some unique features, such as: i) additional bolus administration from anesthesiologist, ii) variable time-delays introduced by data window averaging when poor signal quality is detected, iii) drug trapping from heterogeneous tissue diffusion in high body mass index patients. We successfully reproduced the clinical expected effects of various drugs interacting among the anesthetic and hemodynamic states. Our work is uniquely defined in current state of the art and first of its kind for this application of dose management problem in anesthesia. This simulator provides the research community with accessible tools to allow a systematic design, evaluation and comparison of various control algorithms for multi-drug dosing optimization objectives in anesthesia
Closed-loop control of anesthesia : survey on actual trends, challenges and perspectives
Automation empowers self-sustainable adaptive processes and personalized services in many industries. The implementation of the integrated healthcare paradigm built on Health 4.0 is expected to transform any area in medicine due to the lightning-speed advances in control, robotics, artificial intelligence, sensors etc. The two objectives of this article, as addressed to different entities, are: i) to raise awareness throughout the anesthesiologists about the usefulness of integrating automation and data exchange in their clinical practice for providing increased attention to alarming situations, ii) to provide the actualized insights of drug-delivery research in order to create an opening horizon towards precision medicine with significantly improved human outcomes. This article presents a concise overview on the recent evolution of closed-loop anesthesia delivery control systems by means of control strategies, depth of anesthesia monitors, patient modelling, safety systems, and validation in clinical trials. For decades, anesthesia control has been in the midst of transformative changes, going from simple controllers to integrative strategies of two or more components, but not achieving yet the breakthrough of an integrated system. However, the scientific advances that happen at high speed need a modern review to identify the current technological gaps, societal implications, and implementation barriers. This article provides a good basis for control research in clinical anesthesia to endorse new challenges for intelligent systems towards individualized patient care. At this connection point of clinical and engineering frameworks through (semi-) automation, the following can be granted: patient safety, economical efficiency, and clinicians' efficacy
Robust Control of Maintenance-Phase Anesthesia
In biomedical systems, feedback control can be applied whenever adequate sensors, actuators, and sufficiently accurate mathematical models are available. The key issue is the capacity of the control algorithm to tackle the large levels of uncertainty, both structured and unstructured, associated with patient dynamics. In the particular case of intravenous anesthesia considered here, manipulated variables are drug infusion rates, administered by syringe pumps, and the measured signal outputs are the levels of hypnosis or depth of anesthesia (DoA) and of neuromuscular blockade (NMB). Figure 1 provides an example of a loop closed for the control of NMB
Model predictive control using MISO approach for drug co-administration in anesthesia
In this paper, a model predictive control system for the depth of hypnosis is proposed and analyzed. This approach considers simultaneous co-administration of the hypnotic and analgesic drugs and their effect on the Bispectral Index Scale (BIS). The control scheme uses the nonlinear multiple-input–single-output (MISO) model to predict the remifentanil influence over the propofol hypnotic effect. Then, it exploits a generalized model predictive control algorithm and a ratio between the two drugs in order to provide the optimal dosage for the desired BIS level, taking into account the typical constraints of the process. The proposed approach has been extensively tested in simulation, using a set of patients described by realistic nonlinear pharmacokinetic/pharmacodynamic models, which are representative of a wide population. Additionally, an exhaustive robustness evaluation considering inter- and intra-patient variability has been included, which demonstrates the effectiveness of the analyzed control structure
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