Dynamic Insulin Basal Needs Estimation and Parameters Adjustment in Type 1 Diabetes

Technology advances have made possible improvements such as Continuous Glucose Monitors, giving the patient a glucose reading every few minutes, or insulin pumps, allowing more personalized therapies. With the increasing number of available closed-loop systems, new challenges appear regarding algorithms and functionalities. Several of the analysed systems in this paper try to adapt to changes in some patients’ conditions and, in several of these systems, other variables such as basal needs are considered fixed from day to day to simplify the control problem. Therefore, these systems require a correct adjustment of the basal needs profile which becomes crucial to obtain good results. In this paper a novel approach tries to dynamically determine the insulin basal needs of the patient and use this information within a closed-loop algorithm, allowing the system to dynamically adjust in situations of illness, exercise, high-fat-content meals or even partially blocked infusion sites and avoiding the need for setting a basal profile that approximately matches the basal needs of the patient. The insulin sensitivity factor and the glycemic target are also dynamically modified according to the situation of the patient. Basal insulin needs are dynamically determined through linear regression via the decomposition of previously dosed insulin and its effect on the patient’s glycemia. Using the obtained value as basal insulin needs and other mechanisms such as basal needs modification through its trend, ISF and glycemic targets modification and low-glucose-suspend threshold, the safety of the algorithm is improved. The dynamic basal insulin needs determination was successfully included in a closed-loop control algorithm and was simulated on 30 virtual patients (10 adults, 10 adolescent and 10 children) using an open-source python implementation of the FDA-approved (Food and Drug Administration) UVa (University of Virginia)/Padova Simulator. Simulations showed that the proposed system dynamically determines the basal needs and can adapt to a partial blockage of the insulin infusion, obtaining similar results in terms of time in range to the case in which no blockage was simulated. The proposed algorithm can be incorporated to other current closed-loop control algorithms to directly estimate the patient’s basal insulin needs or as a monitoring channel to detect situations in which basal needs may differ from the expected ones.

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Verification of Closed-loop Systems with Neural Network Controllers

10.29007/btv1 ◽

2019 ◽

Author(s):

Diego Manzanas Lopez ◽

Patrick Musau ◽

Hoang-Dung Tran ◽

Taylor T. Johnson

Keyword(s):

Neural Network ◽

Neural Networks ◽

Control Systems ◽

Closed Loop ◽

Closed Loop Control ◽

Feed Forward ◽

Loop Control ◽

Closed Loop Systems ◽

Feed Forward Neural Networks ◽

Neural Network Controllers

This benchmark suite presents a detailed description of a series of closed-loop control systems with artificial neural network controllers. In many applications, feed-forward neural networks are heavily involved in the implementation of controllers by learning and representing control laws through several methods such as model predictive control (MPC) and reinforcement learning (RL). The type of networks that we consider in this manuscript are feed-forward neural networks consisting of multiple hidden layers with ReLU activation functions and a linear activation function in the output layer. While neural network con- trollers have been able to achieve desirable performance in many contexts, they also present a unique challenge in that it is difficult to provide any guarantees about the correctness of their behavior or reason about the stability a system that employs their use. Thus, from a controls perspective, it is necessary to verify them in conjunction with their corresponding plants in closed-loop. While there have been a handful of works proposed towards the verification of closed-loop systems with feed-forward neural network controllers, this area still lacks attention and a unified set of benchmark examples on which verification techniques can be evaluated and compared. Thus, to this end, we present a range of closed-loop control systems ranging from two to six state variables, and a range of controllers with sizes in the range of eleven neurons to a few hundred neurons in more complex systems.

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Learning robust, real-time, reactive robotic grasping

The International Journal of Robotics Research ◽

10.1177/0278364919859066 ◽

2019 ◽

Vol 39 (2-3) ◽

pp. 183-201 ◽

Cited By ~ 11

Author(s):

Douglas Morrison ◽

Peter Corke ◽

Jürgen Leitner

Keyword(s):

Success Rate ◽

Deep Neural Networks ◽

Closed Loop ◽

State Of The Art ◽

Closed Loop Control ◽

Loop Control ◽

Depth Images ◽

Novel Approach ◽

Grasp Synthesis ◽

Unseen Objects

We present a novel approach to perform object-independent grasp synthesis from depth images via deep neural networks. Our generative grasping convolutional neural network (GG-CNN) predicts a pixel-wise grasp quality that can be deployed in closed-loop grasping scenarios. GG-CNN overcomes shortcomings in existing techniques, namely discrete sampling of grasp candidates and long computation times. The network is orders of magnitude smaller than other state-of-the-art approaches while achieving better performance, particularly in clutter. We run a suite of real-world tests, during which we achieve an 84% grasp success rate on a set of previously unseen objects with adversarial geometry and 94% on household items. The lightweight nature enables closed-loop control of up to 50 Hz, with which we observed 88% grasp success on a set of household objects that are moved during the grasp attempt. We further propose a method combining our GG-CNN with a multi-view approach, which improves overall grasp success rate in clutter by 10%. Code is provided at https://github.com/dougsm/ggcnn

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A Novel Approach to GSA, GA and Wavelet Transform to Design Fuzzy Logic Controller for 1ϕ Multilevel Inverter

International Journal of Power Electronics and Drive Systems (IJPEDS) ◽

10.11591/ijpeds.v7.i4.pp1200-1211 ◽

2016 ◽

Vol 7 (4) ◽

pp. 1200

Author(s):

Varsha Singh ◽

S. Gupta ◽

S. Pattnaik ◽

Aarti Goyal

Keyword(s):

Fuzzy Logic ◽

Wavelet Transform ◽

Output Voltage ◽

Closed Loop ◽

Fuzzy Logic Controller ◽

Multilevel Inverter ◽

Closed Loop Control ◽

Voltage Waveform ◽

Loop Control ◽

Novel Approach

<p>This paper proposes a novel approach for obtaining a closed loop control scheme based on Fuzzy Logic Controller to regulate the output voltage waveform of multilevel inverter. Fuzzy Logic Controller is used to guide and control the inverter to synthesize a stepped output voltage waveform with reduced harmonics. In this paper, three different intelligent soft-computing methods are used to design a fuzzy system to be used as a closed loop control system for regulating the inverter output. Gravitational Search Algorithm and Genetic Algorithm are used as optimization methods to evaluate switching angles for different combination of input voltages applied to MLI. Wavelet Transform is used as synthesizing technique to shape stepped output waveform of inverter using orthogonal wavelet sets. The proposed FLC controlled method is carried out for a wider range of input dc voltages by considering ±10% variations in nominal voltage value. A 7-level inverter is used to validate the results of proposed control methods. The three proposed methods are then compared in terms of various parameters like computational time, switching angles and THD to justify the performance and system flexibility. Finally, hardware based results are also obtained to verify the viability of the proposed method.</p>

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Novel approach to closed-loop control of wire bending machine

2011 IEEE Ninth International Conference on Power Electronics and Drive Systems ◽

10.1109/peds.2011.6147397 ◽

2011 ◽

Cited By ~ 1

Author(s):

Henrik Lavric ◽

Miroslav Bugeza ◽

Rastko Fiser

Keyword(s):

Closed Loop ◽

Closed Loop Control ◽

Loop Control ◽

Novel Approach

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Robotic Telepresence

Proceedings of the Human Factors Society Annual Meeting ◽

10.1177/154193128603000110 ◽

1986 ◽

Vol 30 (1) ◽

pp. 43-44 ◽

Cited By ~ 6

Author(s):

George C. Mohr

Keyword(s):

Cognitive Functions ◽

Closed Loop ◽

Industrial Robot ◽

Space Station ◽

Human Operator ◽

Closed Loop Control ◽

Loop Control ◽

Novel Approach ◽

Operator Tasks ◽

High Level

The Air Force sees a need for a militarized robot, designed to perform flight line maintenance and repair operations during a chemical/biological/radiological attack, or to assist man in space operations such as constructing a space station or performing such tasks as satellite inspection, diagnosis, repair, modification or deactivation. Obviously, these tasks require more than the pre-programmed behavior of an industrial robot. To obtain the high degree of adaptability required, the robot needs either the closed-loop control of a human operator, or a high level “artificial intelligence” capable of emulating human cognitive functions. Robotic telepresence is a novel approach to closed-loop control. By coupling the human operator's visual, tactile, motor and cognitive functions with a remote robot's “head, eyes, and hands,” the human operator is placed effectively “in-the-scene.” With this approach, the natural synergism between the human visual system and hands is exploited to endow the robotic system with human-like capacities to inspect, evaluate, and manipulate. Through robotic telepresence technology, the essential human operator tasks can then be performed in a lethally hazardous environment without exposing the human operator directly.

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Closed-loop control of zebrafish response using a bioinspired robotic-fish in a preference test

Journal of The Royal Society Interface ◽

10.1098/rsif.2012.0540 ◽

2013 ◽

Vol 10 (78) ◽

pp. 20120540 ◽

Cited By ~ 62

Author(s):

Vladislav Kopman ◽

Jeffrey Laut ◽

Giovanni Polverino ◽

Maurizio Porfiri

Keyword(s):

Real Time ◽

Closed Loop ◽

Preference Test ◽

Open Loop ◽

Robotic Fish ◽

Colour Pattern ◽

Closed Loop Control ◽

Water Tank ◽

Loop Control ◽

Closed Loop Systems

In this paper, we study the response of zebrafish to a robotic-fish whose morphology and colour pattern are inspired by zebrafish. Experiments are conducted in a three-chambered instrumented water tank where a robotic-fish is juxtaposed with an empty compartment, and the preference of live subjects is scored as the mean time spent in the vicinity of the tank's two lateral sides. The tail-beating of the robotic-fish is controlled in real-time based on feedback from fish motion to explore a spectrum of closed-loop systems, including proportional and integral controllers. Closed-loop control systems are complemented by open-loop strategies, wherein the tail-beat of the robotic-fish is independent of the fish motion. The preference space and the locomotory patterns of fish for each experimental condition are analysed and compared to understand the influence of real-time closed-loop control on zebrafish response. The results of this study show that zebrafish respond differently to the pattern of tail-beating motion executed by the robotic-fish. Specifically, the preference and behaviour of zebrafish depend on whether the robotic-fish tail-beating frequency is controlled as a function of fish motion and how such closed-loop control is implemented.

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The Impact of Visual Feedback Type on the Mastery of Visuo-Motor Transformations

Zeitschrift für Psychologie ◽

10.1027/2151-2604/a000084 ◽

2012 ◽

Vol 220 (1) ◽

pp. 3-9 ◽

Cited By ~ 4

Author(s):

Sandra Sülzenbrück

Keyword(s):

Empirical Research ◽

Visual Feedback ◽

Closed Loop ◽

Motor Planning ◽

Visual Input ◽

Closed Loop Control ◽

Loop Control ◽

Feedback Type ◽

Effective Use ◽

The Impact

For the effective use of modern tools, the inherent visuo-motor transformation needs to be mastered. The successful adjustment to and learning of these transformations crucially depends on practice conditions, particularly on the type of visual feedback during practice. Here, a review about empirical research exploring the influence of continuous and terminal visual feedback during practice on the mastery of visuo-motor transformations is provided. Two studies investigating the impact of the type of visual feedback on either direction-dependent visuo-motor gains or the complex visuo-motor transformation of a virtual two-sided lever are presented in more detail. The findings of these studies indicate that the continuous availability of visual feedback supports performance when closed-loop control is possible, but impairs performance when visual input is no longer available. Different approaches to explain these performance differences due to the type of visual feedback during practice are considered. For example, these differences could reflect a process of re-optimization of motor planning in a novel environment or represent effects of the specificity of practice. Furthermore, differences in the allocation of attention during movements with terminal and continuous visual feedback could account for the observed differences.

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