Estimation of the breathing effort and relevant lung parameters of a ventilated patient is essential to keep track of a patient's clinical condition. The aim of this paper is to increase estimation accuracy through experiment design. The main method is an experiment design approach across multiple breaths within a linear regression framework to accurately identify the patient's condition. Identifiability and persistence of excitation are used to formulate an estimation problem with a unique solution. Furthermore, Fisher information is used for assessing the parameters sensitivity to slight changes of the ventilator settings to improve the variance of the estimation. The estimation method is applied to simulated patients who breathe regularly but also to patients who have variable breathing patterns. A virtual experiment is conducted for both situations to generate estimation results. The results are analyzed using mathematical tools and show that uniquely estimating the lung parameters and breathing effort over multiple breaths for both regularly and variably breathing patients is possible in the presented framework. The proposed estimation method obtains clinically relevant estimates for a large set of breathing disturbances from the simulation case-study.

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Patient-ventilator asynchrony is one of the largest challenges in mechanical ventilation and is associated with prolonged ICU stay and increased mortality. The aim of this paper is to automatically detect and classify the different types of patient-ventilator asynchronies during a patient's breath using the typically available data on commercially available ventilators. This is achieved by a detection and classification framework using an objective definition of asynchrony and a supervised learning approach. The achieved detection performance of the near-real time framework on a clinical dataset is a significant improvement over current clinical practice, therewith and, this framework has the potential to significantly improve the patient comfort and treatment outcomes.

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Repetitive control (RC) has shown to achieve superior rejection of periodic disturbances. Many nonlinear systems are subject to repeating disturbances. The aim of this article is to develop a continuous-time RC design with stability guarantees for nonlinear Lur'e-type systems. Approximate output tracking is achieved by combining an internal model, consisting of a finite number of linear oscillators with frequencies at the reference frequency and at its multiples, with a stabilizer that guarantees a convergence property of the closed-loop system. The developed RC approach is applied to a nonlinear mechanical ventilation system for intensive care units (ICUs), which can be modeled as a Lur'e-type system. The experimental study confirms that the RC scheme is able to successfully follow the desired target pressure profile to properly support the ventilation needs of an adult patient.

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Estimation of relevant lung parameters and the breathing effort of a ventilated patient is essential to keep track of the patient's clinical condition. The aim of this paper is to investigate the major challenges of estimating the patient's condition with parametric models. The main method is a linear regression framework, where identifiability and persistence of excitation aspects are clearly unraveled. Different approaches for improving estimation accuracy are outlined. As an illustration, one of the solution strategies is implemented, which leads to accurate estimates of the breathing effort and relevant lung parameters.

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In this paper, we introduce a variable-gain control strategy for mechanical ventilators in the respiratory systems. Respiratory systems assist the patients who have difficulty breathing on their own. For the comfort of the patient, fast pressure buildup (and release) and a stable flow response are desired. However, linear controllers typically need to balance between these conflicting objectives. In order to balance this tradeoff in a more desirable manner, a variable-gain controller is proposed, which switches the controller gain based on the magnitude of the patient flow. The effectiveness of the control strategy is demonstrated in experiments on different test lungs.

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In this brief, we introduce a "bandwidth-on-demand" variable-gain control (VGC) strategy that allows for a varying bandwidth of the feedback controller. The proposed VGC can achieve improved performance given time-varying, reference-dependent performance requirements compared with linear time-invariant (LTI) control suffering from design tradeoffs between low-frequency tracking performance and sensitivity to higher-frequency disturbances. The VGC consists of frequency-domain loop-shaped linear filters and a variable-gain element, which depends on reference information. We present easy-to-use controller design guidelines and data-based frequency-domain conditions to verify stability and convergence of the closed-loop system. Moreover, the ability of the ``bandwidth-on-demand'' controller to outperform LTI controllers is emphasized through experiments on an industrial nanopositioning motion stage.@en

It is well-known that fundamental performance limitations exist when using linear feedback control for linear systems. In this note, we present an example of a nonlinear control strategy that can achieve a time-domain performance specification that cannot be obtained by any linear controller. In particular, we present a variable-gain control approach that meets an overshoot requirement that cannot be met by any linear controller.

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In this paper, we introduce the split-path nonlinear integrator (SPANI) as a novel nonlinear filter designed to improve the transient performance of linear systems in terms of overshoot, while preserving good rise-time and settling behavior. In particular, this nonlinear controller targets the well-known trade-off induced by integral action, which removes steady-state errors due to constant external disturbances, but deteriorates transient performance in terms of increased overshoot. The rationale behind the proposed SPANI filter is to ensure that the integral action has, at all times, the same sign as the closed-loop error signal, which, as we will show, enables a reduction in overshoot thereby leading to an overall improved transient performance. The resulting closed-loop dynamics is modeled by a hybrid dynamical system, for which we provide sufficient Lyapunov-based conditions for stability. Furthermore, we illustrate the effectiveness, the design and the tuning of the proposed controller in a benchmark simulation study of an industrial pick-and-place machine.

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