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36 pages, 4586 KB

Open AccessArticle

Human Digital Twin for Personalized Elderly Type 2 Diabetes Management

by Padmapritha Thamotharan, Seshadhri Srinivasan, Jothydev Kesavadev, Gopika Krishnan, Viswanathan Mohan, Subathra Seshadhri, Korkut Bekiroglu and Chiara Toffanin

J. Clin. Med. 2023, 12(6), 2094; https://doi.org/10.3390/jcm12062094 - 7 Mar 2023

Cited by 40 | Viewed by 6169

Abstract

Managing Elderly type 2 diabetes (E-T2D) is challenging due to geriatric conditions (e.g., co-morbidity, multiple drug intake, etc.), and personalization becomes paramount for precision medicine. This paper presents a human digital twin (HDT) framework to manage E-T2D that exploits various patient-specific data and builds a suite of models exploiting the data for prediction and management to personalize diabetes treatment in E-T2D patients. These models include mathematical and deep-learning ones that capture different patient aspects. Consequently, the HDT virtualizes the patient from different viewpoints using an HDT that mimics the patient and has interfaces to update the virtual models simultaneously from measurements. Using these models the HDT obtains deeper insights about the patient. Further, an adaptive patient model fusing this information and a learning-based model predictive control (LB-MPC) algorithm are proposed. The geriatric conditions are captured as model parameters and constraints while solving the LB-MPC to personalize the insulin infusion for E-T2D management. The HDT is deployed on and illustrated with 15 patients using clinical trials and simulations. Our results show that HDT helps improve the time-in-range from 3–75% to 86–97% and reduces insulin infusion by 14–29%. Full article

(This article belongs to the Special Issue Type 2 Diabetes — Pathophysiology, Prevention and Treatment)

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15 pages, 1083 KB

Open AccessArticle

Learning-Based Nonlinear Model Predictive Controller for Hydraulic Cylinder Control of Ship Steering System

by Xiaolong Tang, Changjie Wu and Xiaoyan Xu

J. Mar. Sci. Eng. 2022, 10(12), 2033; https://doi.org/10.3390/jmse10122033 - 19 Dec 2022

Cited by 10 | Viewed by 3964

Abstract

The steering mechanism of ship steering gear is generally driven by a hydraulic system. The precise control of the hydraulic cylinder in the steering mechanism can be achieved by the target rudder angle. However, hydraulic systems are often described as nonlinear systems with uncertainties. Since the system parameters are uncertain and system performances are influenced by disturbances and noises, the robustness cannot be satisfied by approximating the nonlinear theory by a linear theory. In this paper, a learning-based model predictive controller (LB-MPC) is designed for the position control of an electro-hydraulic cylinder system. In order to reduce the influence of uncertainty of the hydraulic system caused by the model mismatch, the Gaussian process (GP) is adopted, and also the real-time input and output data are used to improve the model. A comparative simulation of GP-MPC and MPC is performed assuming that the interference and uncertainty terms are bounded. Consequently, the proposed control strategy can effectively improve the piston position quickly and precisely with multiple constraint conditions. Full article

(This article belongs to the Special Issue Advanced Studies in the Autonomy and Control of Marine Vehicle Systems)

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25 pages, 4924 KB

Open AccessReview

A Survey on Learning-Based Model Predictive Control: Toward Path Tracking Control of Mobile Platforms

by Kanghua Zhang, Jixin Wang, Xueting Xin, Xiang Li, Chuanwen Sun, Jianfei Huang and Weikang Kong

Appl. Sci. 2022, 12(4), 1995; https://doi.org/10.3390/app12041995 - 14 Feb 2022

Cited by 24 | Viewed by 7927

Abstract

The learning-based model predictive control (LB-MPC) is an effective and critical method to solve the path tracking problem in mobile platforms under uncertain disturbances. It is well known that the machine learning (ML) methods use the historical and real-time measurement data to build data-driven prediction models. The model predictive control (MPC) provides an integrated solution for control systems with interactive variables, complex dynamics, and various constraints. The LB-MPC combines the advantages of ML and MPC. In this work, the LB-MPC technique is summarized, and the application of path tracking control in mobile platforms is discussed by considering three aspects, namely, learning and optimizing the prediction model, the controller design, and the controller output under uncertain disturbances. Furthermore, some research challenges faced by LB-MPC for path tracking control in mobile platforms are discussed. Full article

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