EPSAC‐controlled anesthesia with online gain adaptation

Niño, Jorge; Keyser, Robain De; Syafiie, S.; Ionescu, Clara-Mihaela; Struys, Michel

doi:10.1002/acs.1073

Cited by 48 publications

(27 citation statements)

References 30 publications

(39 reference statements)

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“…Hence, the plant model (9) with the MRAC law (11) and the adaptation laẇT = − − e T is globally stable, and accordingly, e,ã q andã r are bounded. Then,̇e is bounded from (14), andV is also bounded from (15). As a consequence,V is uniformly continuous.…”

Section: Two-input-two-output Semi-adaptive Controlmentioning

confidence: 93%

“…However, the majority of prior art has focused on the infusion of a single medication, eg, propofol, [13][14][15] remifentanil, 16,17 and fluid, 18,19 whereas there is only limited volume of work reported on the closed-loop control for infusion of multiple medications to track multiple reference targets (see, for example, previous works [20][21][22] ). In addition to closed-loop control design, the coordination of reference targets also presents a challenge.…”

Section: Introductionmentioning

confidence: 99%

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Coordinated semi‐adaptive closed‐loop control for infusion of two interacting medications

Jin

Kim

Shipley

et al. 2017

Adaptive Control & Signal

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SummaryThis paper presents a coordinated and semi-adaptive closed-loop control approach to the infusion of 2 interacting medications. The proposed approach consists of an upper-level coordination controller and a lower-level semiadaptive controller. The coordination controller recursively adjusts the reference targets based on the estimated dose-response relationship of a patient to ensure that they can be achieved by the patient. The semi-adaptive controller drives the patient outputs to the reference targets while estimating the patient's dose-response relationship online. In this way, the controller is resilient to unachievable caregiver-specified reference targets and responsive to the medication needs of individual patients. To establish the proposed approach, we developed the following: (1) a linear two-input-two-output dose-response model; (2) a two-input-two-output semi-adaptive controller to regulate the patient outputs while adapting high-sensitivity parameters in the patient model; and (3) a coordination controller to adjust the reference targets that reconcile caregiver inputs and medication use. The proposed approach was applied to an example scenario in which cardiac output and respiratory rate are regulated via infusion of propofol and remifentanil in an in silico simulation setting. The results show that the coordinated semi-adaptive control could (1) track achievable reference targets with consistent transient and steady-state performance and (2) resiliently adjust the unachievable reference targets to achievable ones.

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Section: Two-input-two-output Semi-adaptive Controlmentioning

confidence: 93%

Section: Introductionmentioning

confidence: 99%

Coordinated semi‐adaptive closed‐loop control for infusion of two interacting medications

Jin

Kim

Shipley

et al. 2017

Adaptive Control & Signal

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“…LINEAR CONTROL STRATEGIES The default controller available for testing closed loop position control is a P controller with gain scheduling. Gain scheduling algorithms may prove quite effective when used with advanced control strategies [17]; however, due to the increased numerical complexity these cannot be used in this application. The P controller was tuned using trial an error.…”

Section: A Class Of Hammerstein Modelsmentioning

confidence: 99%

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

Ionescu¹,

Keyser²

2016

2016 IEEE International Conference on Automation, Quality and Testing, Robotics (AQTR)

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Abstract-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 leftright movement). Simulation and experimental data from a lab setup suggest that sliding mode control is able to improve global performance parameters.

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“…Due to the fact that PID controllers cannot anticipate to the response of the patient and do not have any knowledge of the drug metabolism, stability problems are present. Therefore, strategies using adaptive (Haddad et al, 2003;Yelneedi et al, 2009;Dumont et al, 2009), predictive (Nunes et al, 2007;Ionescu et al, 2008;Nino et al, 2009) and bayesian-based (De Smet et al, 2008;Struys et al, 2004;Mortier et al, 1998) closed-loop control algorithms have been suggested. Most of these controllers use the Bispectral Index (BIS) signal as a feedback signal for control.…”

Section: Introductionmentioning

confidence: 99%

A No-Nonsense Control Engineering Approach to Anaesthesia Control during Induction Phase

Keyser

Ionescu

2012

IFAC Proceedings Volumes

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This paper introduces a strategy for patient-individualized control of induction phase during general anaesthesia. A down-to-earth, pragmatic (and possibly provocative) approach has been used. The core of the paper is the idea to avoid the direct use of the Bispectral Index signal (BIS) as a feedback signal, since it introduces artificial delays, it is noisy and in the initial stage of the induction it contains no useful information for control purposes because it hardly varies and it introduces nonlinearity. The effect site concentration (C e ) of the drug in the patient is instead used as the controller's feedback signal. Of course the BIS measurement is still indirectly used in our proposed control strategy, in order to adapt the parameters of the Hill curve -which relates BIS to C e -resulting in a patient-individualized closed loop control of anesthesia. These ideas are validated on 12 virtually generated patients varying significantly in sensitivity to the drug effect, using a simple PID control. The control of the induction phase is split into two phases: the initial phase where no useful BIS feedback signal is available, hence target controlled infusion (TCI) with a fixed setpoint is temporarily applied; and the secondary phase when a useful BIS feedback signal becomes available and the TCI setpoint is adapted based on the estimated Hill-curve steepness. The approach is simple and excellent simulation results combined with theoretical insights indicate that the strategy looks promising for future clinical practice.

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EPSAC‐controlled anesthesia with online gain adaptation

Cited by 48 publications

References 30 publications

Coordinated semi‐adaptive closed‐loop control for infusion of two interacting medications

Coordinated semi‐adaptive closed‐loop control for infusion of two interacting medications

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

A No-Nonsense Control Engineering Approach to Anaesthesia Control during Induction Phase

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