Iterative learning control of ventricular assist devices with variable cycle durations

Ketelhut, Maike; Stemmler, Sebastian; Gesenhues, Jonas; Abel, Dirk

doi:10.1016/j.conengprac.2018.10.012

Cited by 41 publications

(24 citation statements)

References 33 publications

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“…On the basis of CEF, a boundary ILC law was proposed in [16] to guarantee the learning convergence. A norm-optimal ILC algorithm incorporating variable cycle durations was developed in [17]. The results confirmed that the algorithm is able to prevent the dilatation of the ventricle and adapt to varying cycle lengths.…”

Section: Introductionmentioning

confidence: 73%

PI-Type Iterative Learning Consensus Control for Second-Order Hyperbolic Distributed Parameter Models Multi-Agent Systems

Liu

Lan

2020

IEEE Access

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This paper considers the consensus control problem of multi-agent systems (MAS) with second-order hyperbolic distributed parameter models. Based on the framework of network topologies, a PItype iterative learning control protocol is proposed by using the nearest neighbor knowledge. Using Gronwall inequality, a sufficient condition for the convergence of the consensus errors with respect to the iteration index is obtained. Finally, the validity of the proposed method is verified by two numerical examples. INDEX TERMS Multi-agent systems, iterative learning control, Gronwall inequality, hyperbolic distributed parameter system.

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Section: Introductionmentioning

confidence: 73%

PI-Type Iterative Learning Consensus Control for Second-Order Hyperbolic Distributed Parameter Models Multi-Agent Systems

Liu

Lan

2020

IEEE Access

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“…Due to the increased necessity of LVADs for clinical use, a wide range of different methods from control engineering has been proposed, such as adaptive, 42 , 65 robust, 48 model predictive, 1 fuzzy logic, 14 proportional integral derivative, 25 sliding mode, 8 and iterative learning control. 34 We refer to Reference 2 for a detailed review and for a discussion on the applicability of these methods in clinical practice.…”

Section: Introductionmentioning

confidence: 99%

An Intra-Cycle Optimal Control Framework for Ventricular Assist Devices Based on Atrioventricular Plane Displacement Modeling

et al. 2021

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A promising treatment for congestive heart failure is the implementation of a left ventricular assist device (LVAD) that works as a mechanical pump. Modern LVADs work with adjustable constant rotor speed and provide therefore continuous blood flow; however, recently undertaken efforts try to mimic pulsatile blood flow by oscillating the pump speed. This work proposes an algorithmic framework to construct and evaluate optimal pump speed policies with respect to generic objectives. We use a model that captures the atrioventricular plane displacement, which is a physiological indicator for heart failure. We employ mathematical optimization to adapt this model to patient specific data and to find optimal pump speed policies with respect to ventricular unloading and aortic valve opening. To this end, we reformulate the cardiovascular dynamics into a switched system and thereby reduce nonlinearities. We consider system switches that stem from varying the constant pump speed and that are state dependent such as valve opening or closing. As a proof of concept study, we personalize the model to a selected patient with respect to ventricular pressure. The model fitting results in a root-mean-square deviation of about 6 mmHg. The optimization that considers aortic valve opening and ventricular unloading results in speed modulation akin to counterpulsation. These in silico findings demonstrate the potential of personalized hemodynamical optimization for the LVAD therapy.

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“…1 Since 1984, it has made great progress in basic theories [2][3][4] and practical applications. [5][6][7][8][9] In ILC, the control objective is to find a system input sequence via iteration learning, which can drive the control system to track the pre-given desired trajectory. The control task is to make use of the available input, output, and state data and the available system dynamics information to design an update mechanism of system input that can realize the control objective.…”

Section: Introductionmentioning

confidence: 99%

Equivalence and convergence of two iterative learning control schemes with state feedback

Zhang

Liu

Ruan

2021

Intl J Robust & Nonlinear

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This article considers the equivalence and convergence of two iterative learning control (ILC) schemes with state feedback for a class of multi-input-multi-output discrete-time nonlinear systems in which each desired trajectory corresponds to a unique desired input or an infinite number of desired inputs. First, we strictly prove that the two ILC schemes are equivalent if they have the identical initial system inputs. Moreover, by the input space transformation method and mathematical induction we establish the necessary and sufficient criterion for the convergence of system output, input and state sequences. It is shown that the (locally Lipschitz) continuity is enough to guarantee the (exponential) convergence. Generally, the learned desired input depends on the values of the initial signals, the learning gain matrix and the state feedback item. By designing them with appropriate structures, the dependence is disappeared. Third, we show how to leverage the available information of system dynamics to design the learning gain matrix and state feedback for better tracking performance. Finally, we provide an example to illustrate the usefulness of our findings, in which we introduce the energy of learnable desired input to evaluate the learning performance.

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Iterative learning control of ventricular assist devices with variable cycle durations

Cited by 41 publications

References 33 publications

PI-Type Iterative Learning Consensus Control for Second-Order Hyperbolic Distributed Parameter Models Multi-Agent Systems

PI-Type Iterative Learning Consensus Control for Second-Order Hyperbolic Distributed Parameter Models Multi-Agent Systems

An Intra-Cycle Optimal Control Framework for Ventricular Assist Devices Based on Atrioventricular Plane Displacement Modeling

Equivalence and convergence of two iterative learning control schemes with state feedback

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