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

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

Model based control strategies for a class of nonlinear mechanical sub-systems

This paper presents a comparison between various control strategies for a class of mechanical actuators common in heavy-duty industry. Typical actuator components are hydraulic or pneumatic elements with static non-linearities, which are commonly referred to as Hammerstein systems. Such static non-linearities may vary in time as a function of the load and hence classical inverse-model based control strategies may deliver sub-optimal performance. This paper investigates the ability of advanced model based control strategies to satisfy a tolerance interval for position error values, overshoot and settling time specifications. Due to the presence of static non-linearity requiring changing direction of movement, control effort is also evaluated in terms of zero crossing frequency (up-down or left-right movement). Simulation and experimental data from a lab setup suggest that sliding mode control is able to improve global performance parameters

Integrated anaesthesia software : data acquisition, controlled infusion schemes and intelligent alarms

Tese de doutoramento. Engenharia Biomédica. Universidade do Porto. Faculdade de Engenharia. 201

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

Towards automation in anaesthesia: a review

Simpósio Internacional MeMeA, realizado em 2014.Surgeries represent a risk for patients and a big cost for the hospital. Anaesthesia represents a complex part of surgery also carries risks for patients. The most known are awareness (with deep psychological consequences); increased risk of morbidity and mortality; adverse reactions and long post-op recovery. The complexity of anaesthesia management can be reduced by studying the patients' responses and developing indicators of the patient state. To assess the level of depth of anaesthesia, the anaesthetist needs to be aware of the patient physiological responses to the drugs and to surgical stimuli. A system that could advise on the patient state considering all clinical signs being measured, the patient individual response and the amount of drugs, will have a big impact on patient overall safety and future health, post-op recovery and hospital resources. This paper does a review of different systems and methods applied to several aspects of the anaesthesia field. All with the goal of working towards automation in this very complex area, that involves high risks for patients. This paper covers advisor systems; signal processing; new monitors and devices; mathematical modelling; and control algorithms; all focused on practical clinical implementation. The objective is to have an overview of the work done so far and the steps taken towards automation in anaesthesia.ISPA - System Integration and Process Automation Unit - Part of the LAETA (Associated Laboratory of Energy, Transports and Aeronautics) a I&D Unit of the Foundation for Science and Technology (FCT), Portugal. FCT support under project PEst-OE/EME/LA0022/2013.info:eu-repo/semantics/publishedVersio