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Objective: The goal of this paper was to optimize robust PID control for propofol anesthesia in children aged 5–10 years to improve performance, particularly to decrease the time of induction of anesthesia while maintaining robustness. Methods: We analyzed results of a previous study conducted by our group to identify opportunities for system improvement. Allometric scaling was introduced to reduce the interpatient variability and a new robust PID controller was designed using an optimization-based method. We evaluated this optimized design in a clinical study involving 16 new cases. Results: The optimized controller design achieved the performance predicted in simulation studies in the design stage. Time of induction of anesthesia was median [Q1, Q3] 3.7 [2.3, 4.1] min and the achieved global score was 13.4 [9.9, 16.8]. Conclusion: Allometric scaling reduces the interpatient variability in this age group and allows for improved closed-loop performance. The uncertainty described by the model set, the predicted closed-loop responses, and the predicted robustness margins are realistic. The system meets the design objectives of improved speed of induction of anesthesia while maintaining robustness and improving clinically relevant system behavior. Significance: Control system optimization and ongoing system improvements are essential to the development of a clinically relevant commercial device. This paper demonstrates the validity of our approach, including system modeling, controller optimization, and pre-clinical testing in simulation. [ABSTRACT FROM AUTHOR]

Discussion of safety preserving control in the presence of uncertainty has mostly focused on stochastic and additive uncertainty. Multiplicative uncertainty can be represented as additive uncertainty, however, this may lead to very conservative solutions. We recently introduced a model-invariant safety-preserving control scheme for multiplicative uncertainty with multi-model description in linear time-invariant systems. This scheme relies on an approximation of the model-invariant viability kernel, which is a set of initial states for which there exists a control input that keeps the states of an uncertain system within a set of viability constraints. In previous work, we showed that the model-invariant viability kernel is the intersection of the viability kernels of all members of a model set representing model-uncertainty. Calculating this intersection is computationally expensive if the number of members in the set is large. In this paper, we propose an efficient approach to under-approximate the model-invariant viability kernel without the need to calculate the viability kernel of all individual set members. This extends the practical applicability of our previous work to multi-model uncertainty descriptions with a large or infinite number of models as well as models with continuous uncertainty. [ABSTRACT FROM AUTHOR]

Abstract Closed-loop anesthesia drug delivery systems can reduce the effect of inter-patient variability, minimize variability in desired clinical effects, and ultimately improve patient safety. Feasibility of closed-loop anesthesia has been clinically evaluated in a number of studies. To obtain regulatory approval to use closed-loop anesthesia as a clinical device, patient safety must be demonstrated. Safety systems for closed-loop anesthesia have been proposed to limit anesthetic concentrations in the plasma and effect-site. Such safety systems minimize the risk of drug under/overdosing. Anesthetic drugs have serious side effects and can significantly reduce blood pressure, thereby jeopardizing patient safety. This paper discusses a safety system that includes safety constraints on blood pressure in addition to the plasma and effect-site concentrations. The proposed safety system is formalized using formal model verification techniques. The performance of closed-loop anesthesia in the presence of constraints on the plasma and effect-site concentrations as well as blood pressure is shown using simulation results. The effectiveness of the proposed safety system is also illustrated in simulation using a realistic clinical scenario. This paper shows that maintaining blood pressure within safety constraints increases the time for anesthesia induction, especially for patients whose blood pressure is more sensitive to anesthetics. For this population, clinical intervention such as administration of vasoactive drugs may be required to counter the effect of propofol on blood pressure during closed-loop anesthesia to achieve a safe yet sufficiently fast induction. This paper extends a previously published formalized safety system which guarantees that the plasma and effect-site concentrations remain within safety limits (Yousefi et al., 2017). Highlights • A formal proof of safety for closed-loop propofol anesthesia is provided. • Closed-loop anesthesia can be performed with minimum risk of drug over/under dosing and hypotension. • Limiting blood pressure increases the induction time of anesthesia. • Administration of vasoactive drugs may be required to have a fast induction of anesthesia while avoiding hypotension. [ABSTRACT FROM AUTHOR]

SUMMARY: In many industrial applications, finding a model from physical laws that is both simple and reliable for control design is a hard and time‐consuming undertaking. When a set of input/output measurements is available, one can derive the controller directly from data, without relying on the knowledge of the physics. In the scientific literature, two main approaches have been proposed for control system design from data. In the ‘model‐based’ approach, a model of the system is first derived from data and then a controller is computed‐based on the model. In the ‘data‐driven’ approach, the controller is directly computed from data. In this work, the previous approaches are compared from a novel perspective. The main finding of the paper is that, although from the standard perspective of parameter variance analysis the model‐based approach is always statistically more efficient, the data‐driven controller might outperform the model‐based solution for what concerns the final control cost. Copyright © 2013 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

Closed-loop control of anesthesia is expected to decrease drug dosage and wake up time while increasing patient safety and decreasing the work load of the anesthesiologist. The potential of closed-loop control in anesthesia has been demonstrated in several clinical studies. One of the challenges in the development of a closed-loop system that can be widely accepted by clinicians and regulatory authorities is the effect of interpatient variability in drug sensitivity. This system uncertainty may lead to unacceptable performance, or even instability of the closed-loop system for some individuals. The development of reliable models of the effect of anesthetic drugs and characterization of the uncertainty is, therefore, an important step in the development of a closed-loop system. Model identification from clinical data is challenging due to limited excitation and the lack of validation data. In this paper, approximate models are validated for controller design by evaluating the predictive accuracy of the closed-loop behavior. A set of 47 validated models that describe the interpatient variability in the response to propofol in children is presented. This model set can be used for robust linear controller design provided that the experimental conditions are similar to the conditions during data collection. [ABSTRACT FROM PUBLISHER]

One of the difficulties in the development of a reliable artificial pancreas for people with type 1 diabetes mellitus (T1DM) is the lack of accurate models of an individual’s response to insulin. Most control algorithms proposed to control the glucose level in subjects with T1DM are model-based. Avoiding postprandial hypoglycemia (<60 mg/dl) while minimizing prandial hyperglycemia (>180 mg/dl) has shown to be difficult in a closed-loop setting due to the patient–model mismatch. In this paper, control-relevant models are developed for T1DM, as opposed to models that minimize a prediction error. The parameters of these models are chosen conservatively to minimize the likelihood of hypoglycemia events. To limit the conservatism due to large intersubject variability, the models are personalized using a priori patient characteristics. The models are implemented in a zone model predictive control algorithm. The robustness of these controllers is evaluated in silico, where hypoglycemia is completely avoided even after large meal disturbances. The proposed control approach is simple and the controller can be set up by a physician without the need for control expertise. [ABSTRACT FROM PUBLISHER]

This paper studies application of different constrained control concepts for the control of Depth of Hypnosis (DOH) in closed-loop anesthesia to guarantee patient safety, while ensuring acceptable tracking performance. The core idea is to formulate Overdosing (OD) prevention and Blood Pressure Decrease (BPD) prevention as operational constraints, and then use a constrained control scheme to enforce the constraints satisfaction at all times. In this paper, three methods are studied: (1) Explicit Reference Governor (ERG), (2) Safety Preserving Control (SPC), and (3) Model Predictive Control (MPC). The performance of the methods is assessed with respect to a simulated surgical procedure for 44 patients whose models have been identified using clinical data. In particular, three realistic clinical scenarios are studied in this paper: (1) normal mode, (2) stimulation, and (3) low clearance. The results demonstrate cons and pros of each method in closed-loop anesthesia according to clinically relevant assessments. [ABSTRACT FROM AUTHOR]

• PID structure as a limiting factor in closed-loop propofol anaesthesia is analysed. • Optimal higher-order LTI controllers are represented using the Youla parameter. • A higher-order controller does not markedly improve the performance versus a PID. • Inter-patient variability is the most substantial limitation to control performance. Background and Objective: New proposals to improve the regulation of hypnosis in anaesthesia based on the development of advanced control structures emerge continuously. However, a fair study to analyse the real benefits of these structures compared to simpler clinically validated PID-based solutions has not been presented so far. The main objective of this work is to analyse the performance limitations associated with using a filtered PID controller, as compared to a high-order controller, represented through a Youla parameter. Methods: The comparison consists of a two-steps methodology. First, two robust optimal filtered PID controllers, considering the effect of the inter-patient variability, are synthesised. A set of 47 validated paediatric pharmacological models, identified from clinical data, is used to this end. This model set provides representative inter-patient variability Second, individualised filtered PID and Youla controllers are synthesised for each model in the set. For fairness of comparison, the same performance objective is optimised for all designs, and the same robustness constraints are considered. Controller synthesis is performed utilising convex optimisation and gradient-based methods relying on algebraic differentiation. The worst-case performance over the patient model set is used for the comparison. Results: Two robust filtered PID controllers for the entire model set, as well as individual-specific PID and Youla controllers, were optimised. All considered designs resulted in similar frequency response characteristics. The performance improvement associated with the Youla controllers was not significant compared to the individually tuned filtered PID controllers. The difference in performance between controllers synthesized for the model set and for individual models was significantly larger than the performance difference between the individual-specific PID and Youla controllers. The different controllers were evaluated in simulation. Although all of them showed clinically acceptable results, the robust solutions provided slower responses. Conclusion: Taking the same clinical and technical considerations into account for the optimisation of the different controllers, the design of individual-specific solutions resulted in only marginal differences in performance when comparing an optimal Youla parameter and its optimal filtered PID counterpart. The inter-patient variability is much more detrimental to performance than the limitations imposed by the simple structure of the filtered PID controller. [ABSTRACT FROM AUTHOR]