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Yan S, Chu LL, Cai Y. Robust H∞ control of T–S fuzzy blood glucose regulation system via adaptive event-triggered scheme. Biomed Signal Process Control 2023. [DOI: 10.1016/j.bspc.2023.104643] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/04/2023]
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2
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Prior-knowledge-embedded model predictive control for blood glucose regulation: Towards efficient and safe artificial pancreas. Biomed Signal Process Control 2023. [DOI: 10.1016/j.bspc.2022.104551] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/06/2023]
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3
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Sachdeva P, M AR, Shukla R, Sahani A. A review on artificial pancreas and regenerative medicine used in the management of Type 1 diabetes mellitus. J Med Eng Technol 2022; 46:693-702. [PMID: 35801984 DOI: 10.1080/03091902.2022.2095049] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/11/2023]
Abstract
Diabetes mellitus is one of the fastest-growing lifestyle disorders in the world. While numerous regimes have been developed to manage diabetes, there continue to be high numbers of diabetes-related deaths worldwide. The review gives a brief introduction to the pathology and aetiology of the disorder, different solutions developed over time with their advantages and disadvantages, and highlights the technological components and challenges of the latest technologies: artificial pancreas and regenerative medicine. The study is restricted to a set of high-quality publications from the last decade.
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Affiliation(s)
- Pallavi Sachdeva
- Department of Biomedical Engineering, Indian Institute of Technology Ropar, Rupnagar, India
| | - Ashrit R M
- Department of Biomedical Engineering, Indian Institute of Technology Ropar, Rupnagar, India
| | - Rahul Shukla
- Department of Biomedical Engineering, Indian Institute of Technology Ropar, Rupnagar, India
| | - Ashish Sahani
- Department of Biomedical Engineering, Indian Institute of Technology Ropar, Rupnagar, India
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Azgomi HF, Faghih RT. Enhancement of Closed-Loop Cognitive Stress Regulation using Supervised Control Architectures. IEEE OPEN JOURNAL OF ENGINEERING IN MEDICINE AND BIOLOGY 2022; 3:7-17. [PMID: 35399789 PMCID: PMC8979622 DOI: 10.1109/ojemb.2022.3143686] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/31/2021] [Revised: 11/06/2021] [Accepted: 12/13/2021] [Indexed: 11/15/2022] Open
Abstract
Goal: We propose novel supervised control architectures to regulate the cognitive stress state and close the loop. Methods: We take information present in underlying neural impulses of skin conductance signals and employ model-based control techniques to close the loop in a state-space framework. For performance enhancement, we establish a supervised knowledge-based layer to update control system in real time. In the supervised architecture, the controller parameters are being updated in real-time. Results: Statistical analyses demonstrate the efficiency of supervised control architectures in improving the closed-loop results while maintaining stress levels within a desired range with more optimized control efforts. The model-based approaches would guarantee the control system-perspective criteria such as stability and optimality, and the proposed supervised knowledge-based layer would further enhance their efficiency. Conclusion: Outcomes in this in silico study verify the proficiency of the proposed supervised architectures to be implemented in the real world.
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Affiliation(s)
- Hamid Fekri Azgomi
- Department of Electrical and Computer EngineeringUniversity of Houston Houston TX 77004 USA
- Department of Neurological SurgeryUniversity of California San Francisco San Francisco CA 94143 USA
| | - Rose T Faghih
- Department of Biomedical EngineeringNew York University New York NY 10010 USA
- Department of Electrical and Computer EngineeringUniversity of Houston Houston TX 77004 USA
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5
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Design of model reference adaptive–PID controller for automated portable duodopa pump in Parkinson’s disease patients. Biomed Signal Process Control 2021. [DOI: 10.1016/j.bspc.2021.102590] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/21/2023]
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6
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Zhang J, Zhang S, Lin P. Event-triggered model predictive control of positive systems with random actuator saturation. NONLINEAR DYNAMICS 2021; 105:417-437. [PMID: 34219968 PMCID: PMC8239486 DOI: 10.1007/s11071-021-06636-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 01/17/2021] [Accepted: 06/13/2021] [Indexed: 06/13/2023]
Abstract
This paper investigates the event-triggered model predictive control of positive systems with actuator saturation. Interval and polytopic uncertainties are imposed on the systems, respectively. First, a new model with actuator saturation obeying Bernoulli distribution is established, which is more general and powerful for describing the saturation phenomenon than the saturation in a deterministic way. Then, a linear event-triggering condition is constructed based on the state and error signal. Under the event-triggering condition, an interval estimate approach is presented to reach the positivity and stability of the systems. The saturation part in the controller is technically transformed into a non-saturation part. Thus, a linear programming approach is proposed to compute the event-triggered controller gain and the corresponding gain of attraction domain. A predictive algorithm is introduced for the computation of the event-triggered controller parameters. Finally, an example is provided to illustrate the validity of the design.
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Affiliation(s)
- Junfeng Zhang
- School of Automation, Hangzhou Dianzi University, Hangzhou, 310018 China
| | - Suhuan Zhang
- School of Automation, Hangzhou Dianzi University, Hangzhou, 310018 China
| | - Peng Lin
- School of Automation, Hangzhou Dianzi University, Hangzhou, 310018 China
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7
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Lee S, Kim J, Park SW, Jin SM, Park SM. Toward a Fully Automated Artificial Pancreas System Using a Bioinspired Reinforcement Learning Design: In Silico Validation. IEEE J Biomed Health Inform 2021; 25:536-546. [PMID: 32750935 DOI: 10.1109/jbhi.2020.3002022] [Citation(s) in RCA: 17] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/07/2022]
Abstract
OBJECTIVE The automation of insulin treatment is the most challenge aspect of glucose management for type 1 diabetes owing to unexpected exogenous events (e.g., meal intake). In this article, we propose a novel reinforcement learning (RL) based artificial intelligence (AI) algorithm for a fully automated artificial pancreas (AP) system. METHODS A bioinspired RL designing method was developed for automated insulin infusion. This method has reward functions that imply the temporal homeostatic objective and discount factors that reflect an individual specific pharmacological characteristic. The proposed method was applied to a training method using an RL algorithm and was evaluated in virtual patients from the FDA approved UVA/Padova simulator with unannounced meal intakes. RESULTS For a single-meal experiment with preprandial fasting, the trained policy demonstrated fully automated regulation in both the basal and postprandial phases. In the in silico trial with a variation of insulin sensitivity and dawn phenomenon, the policy achieved a mean glucose of 124.72 mg/dL and percentage time in the normal range of 89.56%. The layer-wise relevance propagation provides interpretable information on AI-driven decision for robustness to sensor noise, automated postprandial regulation, and insulin stacking avoidance. CONCLUSION The AP algorithm based on the bioinspired RL approach enables fully automated blood glucose control with unannounced meal intake. SIGNIFICANCE The proposed framework can be extended to other drug-based treatments for systems with significant uncertainties.
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Goez-Mora JE, Villa-Tamayo MF, Vallejo M, Rivadeneira PS. Performance Analysis of Different Embedded Systems and Open-Source Optimization Packages Towards an Impulsive MPC Artificial Pancreas. Front Endocrinol (Lausanne) 2021; 12:662348. [PMID: 33981286 PMCID: PMC8109177 DOI: 10.3389/fendo.2021.662348] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/01/2021] [Accepted: 03/19/2021] [Indexed: 11/23/2022] Open
Abstract
Current technological advances have brought closer to reality the project of a safe, portable, and efficient artificial pancreas for people with type 1 diabetes (T1D). Among the developed control strategies for T1D, model predictive control (MPC) has been emphasized in literature as a promising control for glucose regulation. However, these control strategies are commonly designed in a computer environment, regardless of the limitations of a portable device. In this paper, the performances of six embedded platforms and three open-source optimization solver algorithms are assessed for T1D treatment. Their advantages and limitations are clarified using four MPC formulations of increasing complexity and a hardware-in-the-loop methodology to evaluate glucose control in virtual adult subjects. The performance comparison includes the execution time, the difference concerning the evolution obtained in MATLAB, the processor temperature, energy consumption, time percentage in normoglycemia, and the number of hypo- and hyperglycemic events. Results show that Quadprog is the package that faithfully follows the results obtained with control strategies designed and tuned on a computer with the MATLAB software. In addition, the Raspberry Pi 3 and the Tinker Board S embedded systems present the appropriate characteristics to be implemented as portable devices in the artificial pancreas application according to the criteria set out in this work.
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9
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Chakrabarty A, Healey E, Shi D, Zavitsanou S, Doyle FJ, Dassau E. Embedded Model Predictive Control for a Wearable Artificial Pancreas. IEEE TRANSACTIONS ON CONTROL SYSTEMS TECHNOLOGY : A PUBLICATION OF THE IEEE CONTROL SYSTEMS SOCIETY 2020; 28:2600-2607. [PMID: 33762804 PMCID: PMC7983018 DOI: 10.1109/tcst.2019.2939122] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/12/2023]
Abstract
While artificial pancreas (AP) systems are expected to improve the quality of life among people with type 1 diabetes mellitus (T1DM), the design of convenient systems that optimize the user experience, especially for those with active lifestyles, such as children and adolescents, still remains an open research question. In this work, we introduce an embeddable design and implementation of model predictive control (MPC) of AP systems for people with T1DM that significantly reduces the weight and on-body footprint of the AP system. The embeddable controller is based on a zone MPC that has been evaluated in multiple clinical studies. The proposed embedded zone MPC features a simpler design of the periodic safe zone in the cost function and the utilization of state-of-the-art alternating minimization algorithms for solving the convex programming problems inherent to MPC with linear models subject to convex constraints. Off-line closed-loop data generated by the FDA-accepted UVA/Padova simulator is used to select an optimization algorithm and corresponding tuning parameters. Through hardware-in-the-loop in silico results on a limited-resource Arduino Zero (Feather M0) platform, we demonstrate the potential of the proposed embedded MPC. In spite of resource limitations, our embedded zone MPC manages to achieve comparable performance of that of the full-version zone MPC implemented in a 64-bit desktop for scenarios with/without meal-disturbance compensations. Metrics for performance comparison included median percent time in the euglycemic ([70, 180] mg/dL range) of 84.3% vs. 83.1% for announced meals, with an equivalence test yielding p = 0.0013 and 66.2% vs. 66.0% for unannounced meals with p = 0.0028.
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Affiliation(s)
- Ankush Chakrabarty
- Control and Dynamical Systems Group, Mitsubishi Electric Research Laboratories, Cambridge, MA, USA
| | - Elizabeth Healey
- Harvard John A. Paulson School of Engineering and Applied Sciences, Harvard University, Cambridge, MA 02138, USA
| | - Dawei Shi
- Harvard John A. Paulson School of Engineering and Applied Sciences, Harvard University, Cambridge, MA 02138, USA
| | - Stamatina Zavitsanou
- Harvard John A. Paulson School of Engineering and Applied Sciences, Harvard University, Cambridge, MA 02138, USA
| | - Francis J. Doyle
- Harvard John A. Paulson School of Engineering and Applied Sciences, Harvard University, Cambridge, MA 02138, USA
| | - Eyal Dassau
- Harvard John A. Paulson School of Engineering and Applied Sciences, Harvard University, Cambridge, MA 02138, USA
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10
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A new fractional-order general type-2 fuzzy predictive control system and its application for glucose level regulation. Appl Soft Comput 2020. [DOI: 10.1016/j.asoc.2020.106241] [Citation(s) in RCA: 36] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
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11
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Clairon Q, Henderson R, Young NJ, Wilson ED, Taylor CJ. Adaptive treatment and robust control. Biometrics 2020; 77:223-236. [PMID: 32249926 DOI: 10.1111/biom.13268] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/04/2018] [Revised: 01/23/2020] [Accepted: 03/24/2020] [Indexed: 11/28/2022]
Abstract
A control theory perspective on determination of optimal dynamic treatment regimes is considered. The aim is to adapt statistical methodology that has been developed for medical or other biostatistical applications to incorporate powerful control techniques that have been designed for engineering or other technological problems. Data tend to be sparse and noisy in the biostatistical area and interest has tended to be in statistical inference for treatment effects. In engineering fields, experimental data can be more easily obtained and reproduced and interest is more often in performance and stability of proposed controllers rather than modeling and inference per se. We propose that modeling and estimation should be based on standard statistical techniques but subsequent treatment policy should be obtained from robust control. To bring focus, we concentrate on A-learning methodology as developed in the biostatistical literature and H ∞ -synthesis from control theory. Simulations and two applications demonstrate robustness of the H ∞ strategy compared to standard A-learning in the presence of model misspecification or measurement error.
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Affiliation(s)
- Q Clairon
- Bordeaux Population Health Research Center, Inria Bordeaux Sud-Ouest, Inserm, University of Bordeaux, Bordeaux, France
| | - R Henderson
- School of Mathematics, Statistics and Physics, Newcastle University, UK
| | - N J Young
- School of Mathematics, Statistics and Physics, Newcastle University, UK
| | - E D Wilson
- School of Computing and Communications, Lancaster University, Lancaster, UK
| | - C J Taylor
- Department of Engineering, Lancaster University, Lancaster, UK
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Yu X, Rashid M, Feng J, Hobbs N, Hajizadeh I, Samadi S, Sevil M, Lazaro C, Maloney Z, Littlejohn E, Quinn L, Cinar A. Online Glucose Prediction Using Computationally Efficient Sparse Kernel Filtering Algorithms in Type-1 Diabetes. IEEE TRANSACTIONS ON CONTROL SYSTEMS TECHNOLOGY : A PUBLICATION OF THE IEEE CONTROL SYSTEMS SOCIETY 2020; 28:3-15. [PMID: 32699492 PMCID: PMC7375403 DOI: 10.1109/tcst.2018.2843785] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/11/2023]
Abstract
Streaming data from continuous glucose monitoring (CGM) systems enable the recursive identification of models to improve estimation accuracy for effective predictive glycemic control in patients with type-1 diabetes. A drawback of conventional recursive identification techniques is the increase in computational requirements, which is a concern for online and real-time applications such as the artificial pancreas systems implemented on handheld devices and smartphones where computational resources and memory are limited. To improve predictions in such computationally constrained hardware settings, efficient adaptive kernel filtering algorithms are developed in this paper to characterize the nonlinear glycemic variability by employing a sparsification criterion based on the information theory to reduce the computation time and complexity of the kernel filters without adversely deteriorating the predictive performance. Furthermore, the adaptive kernel filtering algorithms are designed to be insensitive to abnormal CGM measurements, thus compensating for measurement noise and disturbances. As such, the sparsification-based real-time model update framework can adapt the prediction models to accurately characterize the time-varying and nonlinear dynamics of glycemic measurements. The proposed recursive kernel filtering algorithms leveraging sparsity for improved computational efficiency are applied to both in-silico and clinical subjects, and the results demonstrate the effectiveness of the proposed methods.
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Affiliation(s)
- Xia Yu
- School of Information Science and Engineering, Northeastern University, Shenyang 110819, China
| | - Mudassir Rashid
- Department of Chemical and Biological Engineering, Illinois Institute of Technology, Chicago, IL 60616 USA
| | - Jianyuan Feng
- Department of Chemical and Biological Engineering, Illinois Institute of Technology, Chicago, IL 60616 USA
| | - Nicole Hobbs
- Department of Biomedical Engineering, Illinois Institute of Technology, Chicago, IL 60616 USA
| | - Iman Hajizadeh
- Department of Chemical and Biological Engineering, Illinois Institute of Technology, Chicago, IL 60616 USA
| | - Sediqeh Samadi
- Department of Chemical and Biological Engineering, Illinois Institute of Technology, Chicago, IL 60616 USA
| | - Mert Sevil
- Department of Biomedical Engineering, Illinois Institute of Technology, Chicago, IL 60616 USA
| | - Caterina Lazaro
- Department of Electrical and Computer Engineering, Illinois Institute of Technology, Chicago, IL 60616 USA
| | - Zacharie Maloney
- Department of Electrical and Computer Engineering, Illinois Institute of Technology, Chicago, IL 60616 USA
| | - Elizabeth Littlejohn
- Kovler Diabetes Center, Department of Pediatrics and Medicine, University of Chicago, Chicago, IL 60637 USA
| | - Laurie Quinn
- Department of Biobehavioral Health Science, College of Nursing, University of Illinois at Chicago, Chicago, IL 60612 USA
| | - Ali Cinar
- Department of Chemical and Biological Engineering, Illinois Institute of Technology, Chicago, IL 60616 USA, and also with the Department of Biomedical Engineering, Illinois Institute of Technology, Chicago, IL 60616 USA
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13
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Sharifi N, Zhou Y, Holmes G, Chen Y. Overcoming Channel Uncertainties in Touchable Molecular Communication for Direct-Drug-Targeting-Assisted Immuno-Chemotherapy. IEEE Trans Nanobioscience 2019; 19:249-258. [PMID: 31841419 DOI: 10.1109/tnb.2019.2960068] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/07/2022]
Abstract
OBJECTIVE The performance of targeted immuno-chemotherapy of tumor is highly exposed to drug absorption in systemic circulation, which reduces its efficiency and increases side-effects. Direct drug targeting (DDT) combined with immuno-chemotherapy has the potential to mitigate the undesired systemic exposure, by using drug-loaded nanorobots to target cancer cells with the shortest possible physiological routes. This process can be modeled by the "touchable" (i.e., externally controllable and trackable) molecular communication system. However, such a complex process still suffers from various pharmacokinetic uncertainties caused by diffusion, degeneration, and branching of nanorobots (DDT pharmacokinetic uncertainties), as well as tumor/immune system modeling errors. The current work aims at identifying optimal drug administration plans by overcoming such challenges. METHODS A revisited tumor-immune interaction model is proposed to incorporate randomness of the drug concentration in the tumor site. Then, a robust multiple model predictive control (MMPC) scheme for the proposed tumor-immune interaction model is designed that uses multiple system models and an adaptive switch to identify the optimal plans for mixed drug administration via drug-loaded nanorobots. Furthermore, a wide range of prediction horizons under different loss scenarios of drug-loaded nanorobots and system model mismatches have been investigated in order to identify safe operating regions. From the molecular communications paradigm, this can be considered as a more robust information transmission system with feedback of channel state information to the transmitter implemented in the control unit. RESULTS The efficacy of the proposed MMPC is illustrated through identification of globally optimized drug administration schedules subject to various controller operation imperfections, which lead to successful cancer treatment as demonstrated through computational experiments. CONCLUSION By combining DDT with conventional mixed immunotherapy and chemotherapy, the proposed robust MMPC offers promising performance in controlling tumor growth while keeping the immune cell density higher than a specific level in the presence of both DDT pharmacokinetic uncertainties and system model mismatches. SIGNIFICANCE We believe that the proposed design framework represents an important first step towards clinically relevant DDT in the combined immunotherapy and chemotherapy of tumor given its robust performance.
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Hajizadeh I, Hobbs N, Samadi S, Sevil M, Rashid M, Brandt R, Askari MR, Maloney Z, Cinar A. Controlling the AP Controller: Controller Performance Assessment and Modification. J Diabetes Sci Technol 2019; 13:1091-1104. [PMID: 31561714 PMCID: PMC6835190 DOI: 10.1177/1932296819877217] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/15/2022]
Abstract
BACKGROUND Despite recent advances in closed-loop control of blood glucose concentration (BGC) in people with type 1 diabetes (T1D), online performance assessment and modification of artificial pancreas (AP) control systems remain a challenge as the metabolic characteristics of users change over time. METHODS A controller performance assessment and modification system (CPAMS) analyzes the glucose concentration variations and controller behavior, and modifies the parameters of the control system used in the multivariable AP system. Various indices are defined to quantitatively evaluate the controller performance in real time. Controller performance assessment and modification system also incorporates online learning from historical data to anticipate impending disturbances and proactively counteract their effects. RESULTS Using a multivariable simulation platform for T1D, the CPAMS is used to enhance the BGC regulation in people with T1D by means of automated insulin delivery with an adaptive learning predictive controller. Controller performance assessment and modification system increases the percentage of time in the target range (70-180) mg/dL by 52.3% without causing any hypoglycemia and hyperglycemia events. CONCLUSIONS The results demonstrate a significant improvement in the multivariable AP controller performance by using CPAMS.
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Affiliation(s)
- Iman Hajizadeh
- Department of Chemical and Biological Engineering, Illinois Institute of Technology, Chicago, IL, USA
| | - Nicole Hobbs
- Department of Biomedical Engineering, Illinois Institute of Technology, Chicago, IL, USA
| | - Sediqeh Samadi
- Department of Chemical and Biological Engineering, Illinois Institute of Technology, Chicago, IL, USA
| | - Mert Sevil
- Department of Biomedical Engineering, Illinois Institute of Technology, Chicago, IL, USA
| | - Mudassir Rashid
- Department of Chemical and Biological Engineering, Illinois Institute of Technology, Chicago, IL, USA
| | - Rachel Brandt
- Department of Biomedical Engineering, Illinois Institute of Technology, Chicago, IL, USA
| | - Mohammad Reza Askari
- Department of Chemical and Biological Engineering, Illinois Institute of Technology, Chicago, IL, USA
| | - Zacharie Maloney
- Department of Biomedical Engineering, Illinois Institute of Technology, Chicago, IL, USA
| | - Ali Cinar
- Department of Chemical and Biological Engineering, Illinois Institute of Technology, Chicago, IL, USA
- Department of Biomedical Engineering, Illinois Institute of Technology, Chicago, IL, USA
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15
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Hajizadeh I, Samadi S, Sevil M, Rashid M, Cinar A. Performance Assessment and Modification of an Adaptive Model Predictive Control for Automated Insulin Delivery by a Multivariable Artificial Pancreas. Ind Eng Chem Res 2019. [DOI: 10.1021/acs.iecr.8b06202] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/24/2022]
Affiliation(s)
- Iman Hajizadeh
- Department of Chemical and Biological Engineering, Illinois Institute of Technology, Chicago, Illinois 60616, United States
| | - Sediqeh Samadi
- Department of Chemical and Biological Engineering, Illinois Institute of Technology, Chicago, Illinois 60616, United States
| | - Mert Sevil
- Department of Biomedical Engineering, Illinois Institute of Technology, Chicago, Illinois 60616, United States
| | - Mudassir Rashid
- Department of Chemical and Biological Engineering, Illinois Institute of Technology, Chicago, Illinois 60616, United States
| | - Ali Cinar
- Department of Chemical and Biological Engineering, Illinois Institute of Technology, Chicago, Illinois 60616, United States
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16
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Hajizadeh I, Rashid M, Cinar A. Plasma-Insulin-Cognizant Adaptive Model Predictive Control for Artificial Pancreas Systems. JOURNAL OF PROCESS CONTROL 2019; 77:97-113. [PMID: 31814659 PMCID: PMC6897508 DOI: 10.1016/j.jprocont.2019.03.009] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/06/2023]
Abstract
An adaptive model predictive control (MPC) algorithm with dynamic adjustments of constraints and objective function weights based on estimates of the plasma insulin concentration (PIC) is proposed for artificial pancreas (AP) systems. A personalized compartment model that translates the infused insulin into estimates of PIC is integrated with a recursive subspace-based system identification to characterize the transient dynamics of glycemic measurements. The system identification approach is able to identify stable, reliable linear time-varying models from closed-loop data. An MPC algorithm using the adaptive models is designed to compute the optimal exogenous insulin delivery for AP systems without requiring any manually-entered meal information. A dynamic safety constraint derived from the estimation of PIC is incorporated in the adaptive MPC to improve the efficacy of the AP and prevent insulin overdosing. Simulation case studies demonstrate the performance of the proposed adaptive MPC algorithm.
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Affiliation(s)
- Iman Hajizadeh
- Dept of Chemical and Biological Engineering, Illinois Institute of Technology, Chicago, IL 60616
| | - Mudassir Rashid
- Dept of Chemical and Biological Engineering, Illinois Institute of Technology, Chicago, IL 60616
| | - Ali Cinar
- Dept of Chemical and Biological Engineering, Illinois Institute of Technology, Chicago, IL 60616
- Dept of Biomedical Engineering, Illinois Institute of Technology, Chicago, IL 60616
- Correspondence concerning this article should be addressed to A. Cinar at
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17
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Chakrabarty A, Gregory JM, Moore LM, Williams PE, Farmer B, Cherrington AD, Lord P, Shelton B, Cohen D, Zisser HC, Doyle FJ, Dassau E. A New Animal Model of Insulin-Glucose Dynamics in the Intraperitoneal Space Enhances Closed-Loop Control Performance. JOURNAL OF PROCESS CONTROL 2019; 76:62-73. [PMID: 31178632 PMCID: PMC6548466 DOI: 10.1016/j.jprocont.2019.01.002] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/31/2023]
Abstract
Current artificial pancreas systems (AP) operate via subcutaneous (SC) glucose sensing and SC insulin delivery. Due to slow diffusion and transport dynamics across the interstitial space, even the most sophisticated control algorithms in on-body AP systems cannot react fast enough to maintain tight glycemic control under the effect of exogenous glucose disturbances caused by ingesting meals or performing physical activity. Recent efforts made towards the development of an implantable AP have explored the utility of insulin infusion in the intraperitoneal (IP) space: a region within the abdominal cavity where the insulin-glucose kinetics are observed to be much more rapid than the SC space. In this paper, a series of canine experiments are used to determine the dynamic association between IP insulin boluses and plasma glucose levels. Data from these experiments are employed to construct a new mathematical model and to formulate a closed-loop control strategy to be deployed on an implantable AP. The potential of the proposed controller is demonstrated via in-silico experiments on an FDA-accepted benchmark cohort: the proposed design significantly outperforms a previous controller designed using artificial data (time in clinically acceptable glucose range: 97.3±1.5% vs. 90.1±5.6%). Furthermore, the robustness of the proposed closed-loop system to delays and noise in the measurement signal (for example, when glucose is sensed subcutaneously) and deleterious glycemic changes (such as sudden glucose decline due to physical activity) is investigated. The proposed model based on experimental canine data leads to the generation of more effective control algorithms and is a promising step towards fully automated and implantable artificial pancreas systems.
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Affiliation(s)
- Ankush Chakrabarty
- Harvard John A. Paulson School of Engineering and Applied Sciences, Harvard University, Cambridge, MA
| | | | - L. Merkle Moore
- Department of Molecular Physiology and Biophysics, Vanderbilt University School of Medicine, Nashville, TN
| | - Philip E. Williams
- Section of Surgical Sciences, Vanderbilt University School of Medicine, Nashville, TN
| | - Ben Farmer
- Department of Molecular Physiology and Biophysics, Vanderbilt University School of Medicine, Nashville, TN
| | - Alan D. Cherrington
- Department of Molecular Physiology and Biophysics, Vanderbilt University School of Medicine, Nashville, TN
| | | | | | - Don Cohen
- Physiologic Devices, Inc., Alpine, CA
| | - Howard C. Zisser
- Department of Chemical Engineering, University of California Santa Barbara, Santa Barbara, CA
| | - Francis J. Doyle
- Harvard John A. Paulson School of Engineering and Applied Sciences, Harvard University, Cambridge, MA
| | - Eyal Dassau
- Harvard John A. Paulson School of Engineering and Applied Sciences, Harvard University, Cambridge, MA
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Ramkissoon CM, Bertachi A, Beneyto A, Bondia J, Vehi J. Detection and Control of Unannounced Exercise in the Artificial Pancreas Without Additional Physiological Signals. IEEE J Biomed Health Inform 2019; 24:259-267. [PMID: 30763250 DOI: 10.1109/jbhi.2019.2898558] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Abstract
The purpose of this study was to develop an algorithm that detects aerobic exercise and triggers disturbance rejection actions to prevent exercise-induced hypoglycemia. This approach can provide a solution to poor glycemic control during and after aerobic exercise, a major hindrance in the participation of exercise by patients with type 1 diabetes. This novel exercise-induced hypoglycemia reduction algorithm (EHRA) detects exercise using a threshold on a disturbance term, a parameter estimated from an augmented minimal model using an unscented Kalman filter. After detection, the EHRA triggers the following three actions: First, a carbohydrate suggestion, second, a reduction in basal insulin and the insulin-on-board maximum limit, and finally, a 30% reduction of the next insulin meal bolus. The EHRA was tested in silico using a 15-day scenario with 8 exercise sessions of 50 min at [Formula: see text] on alternating days. The EHRA was able to obtain improved results when compared to strategies with and without exercise announcement. The unannounced, announced, and EHRA strategies all obtained an overall percentage of time in range (70-180 mg/dl) of 94% and a percentage of time 70 mg/dl of 2%, 0%, and 0%, respectively. The EHRA was tested for robustness during exercise sessions of +25% and -25% intensity and results suggest that the EHRA is able to account for variability in exercise intensity, duration, and patient dynamics such as glucose uptake rate and insulin sensitivity.
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Sensor-based detection and estimation of meal carbohydrates for people with diabetes. Biomed Signal Process Control 2019. [DOI: 10.1016/j.bspc.2018.09.012] [Citation(s) in RCA: 16] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/22/2022]
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Shi D, Dassau E, Doyle FJ. Adaptive Zone Model Predictive Control of Artificial Pancreas Based on Glucose- and Velocity-Dependent Control Penalties. IEEE Trans Biomed Eng 2018; 66:1045-1054. [PMID: 30142748 DOI: 10.1109/tbme.2018.2866392] [Citation(s) in RCA: 25] [Impact Index Per Article: 4.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/27/2023]
Abstract
OBJECTIVE Zone model predictive control (MPC) has been proven to be an efficient approach to closed-loop insulin delivery in clinical studies. In this paper, we aim to safely reduce mean glucose levels by proposing control penalty adaptation in the cost function of zone MPC. METHODS A zone MPC method with a dynamic cost function that updates its control penalty parameters in real time according to the predicted glucose and its rate of change is developed. The proposed method is evaluated on the entire 100-adult cohort of the FDA-accepted UVA/Padova T1DM simulator and compared with the zone MPC tested in an extended outpatient study. RESULTS For unannounced meals, the proposed method leads to statistically significant improvements in terms of mean glucose (153.8 mg/dL vs. 159.0 mg/dL; ) and percentage time in [70, 180] mg/dL ([Formula: see text] vs. [Formula: see text]; ) without increasing the risk of hypoglycemia. Performance for announced meals is similar to that obtained without adaptation. The proposed method also behaves properly and safely for scenarios of moderate meal-bolus and basal rate mismatches, as well as simulated unannounced exercise. Advisory-mode analysis based on clinical data indicates that the method can reduce glucose levels through suggesting additional safe amounts of insulin on top of those suggested by the zone MPC used in the study. CONCLUSION The proposed method leads to improved glucose control without increasing hypoglycemia risks. SIGNIFICANCE The results validate the feasibility of improving glucose regulation through glucose- and velocity-dependent control penalty adaptation in MPC design.
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Hajizadeh I, Rashid M, Samadi S, Feng J, Sevil M, Hobbs N, Lazaro C, Maloney Z, Brandt R, Yu X, Turksoy K, Littlejohn E, Cengiz E, Cinar A. Adaptive and Personalized Plasma Insulin Concentration Estimation for Artificial Pancreas Systems. J Diabetes Sci Technol 2018; 12:639-649. [PMID: 29566547 PMCID: PMC6154239 DOI: 10.1177/1932296818763959] [Citation(s) in RCA: 24] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
Abstract
BACKGROUND The artificial pancreas (AP) system, a technology that automatically administers exogenous insulin in people with type 1 diabetes mellitus (T1DM) to regulate their blood glucose concentrations, necessitates the estimation of the amount of active insulin already present in the body to avoid overdosing. METHOD An adaptive and personalized plasma insulin concentration (PIC) estimator is designed in this work to accurately quantify the insulin present in the bloodstream. The proposed PIC estimation approach incorporates Hovorka's glucose-insulin model with the unscented Kalman filtering algorithm. Methods for the personalized initialization of the time-varying model parameters to individual patients for improved estimator convergence are developed. Data from 20 three-days-long closed-loop clinical experiments conducted involving subjects with T1DM are used to evaluate the proposed PIC estimation approach. RESULTS The proposed methods are applied to the clinical data containing significant disturbances, such as unannounced meals and exercise, and the results demonstrate the accurate real-time estimation of the PIC with the root mean square error of 7.15 and 9.25 mU/L for the optimization-based fitted parameters and partial least squares regression-based testing parameters, respectively. CONCLUSIONS The accurate real-time estimation of PIC will benefit the AP systems by preventing overdelivery of insulin when significant insulin is present in the bloodstream.
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Affiliation(s)
- Iman Hajizadeh
- Department of Chemical and Biological
Engineering, Illinois Institute of Technology, Chicago, IL, USA
| | - Mudassir Rashid
- Department of Chemical and Biological
Engineering, Illinois Institute of Technology, Chicago, IL, USA
| | - Sediqeh Samadi
- Department of Chemical and Biological
Engineering, Illinois Institute of Technology, Chicago, IL, USA
| | - Jianyuan Feng
- Department of Chemical and Biological
Engineering, Illinois Institute of Technology, Chicago, IL, USA
| | - Mert Sevil
- Department of Biomedical Engineering,
Illinois Institute of Technology, Chicago, IL, USA
| | - Nicole Hobbs
- Department of Biomedical Engineering,
Illinois Institute of Technology, Chicago, IL, USA
| | - Caterina Lazaro
- Department of Electrical and Computer
Engineering, Illinois Institute of Technology, Chicago, IL, USA
| | - Zacharie Maloney
- Department of Biomedical Engineering,
Illinois Institute of Technology, Chicago, IL, USA
| | - Rachel Brandt
- Department of Biomedical Engineering,
Illinois Institute of Technology, Chicago, IL, USA
| | - Xia Yu
- School of Information Science and
Technology, Northeastern University, Shenyang, China
| | - Kamuran Turksoy
- Department of Biomedical Engineering,
Illinois Institute of Technology, Chicago, IL, USA
| | - Elizabeth Littlejohn
- Department of Pediatrics and Medicine,
Section of Endocrinology, Kovler Diabetes Center, University of Chicago, Chicago,
IL, USA
| | - Eda Cengiz
- Department of Pediatrics, Yale
University School of Medicine, New Haven, CT, USA
| | - Ali Cinar
- Department of Chemical and Biological
Engineering, Illinois Institute of Technology, Chicago, IL, USA
- Department of Biomedical Engineering,
Illinois Institute of Technology, Chicago, IL, USA
- Ali Cinar, PhD, Illinois Institute of
Technology, Department of Chemical and Biological Engineering, 10 W 33rd St,
Chicago, IL 60616, USA.
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Abstract
PURPOSE OF REVIEW The review summarizes the current state of the artificial pancreas (AP) systems and introduces various new modules that should be included in future AP systems. RECENT FINDINGS A fully automated AP must be able to detect and mitigate the effects of meals, exercise, stress and sleep on blood glucose concentrations. This can only be achieved by using a multivariable approach that leverages information from wearable devices that provide real-time streaming data about various physiological variables that indicate imminent changes in blood glucose concentrations caused by meals, exercise, stress and sleep. The development of a fully automated AP will necessitate the design of multivariable and adaptive systems that use information from wearable devices in addition to glucose sensors and modify the models used in their model-predictive alarm and control systems to adapt to the changes in the metabolic state of the user. These AP systems will also integrate modules for controller performance assessment, fault detection and diagnosis, machine learning and classification to interpret various signals and achieve fault-tolerant control. Advances in wearable devices, computational power, and safe and secure communications are enabling the development of fully automated multivariable AP systems.
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Affiliation(s)
- Ali Cinar
- Department of Chemical and Biological Engineering and Department of Biomedical Engineering, Illinois Institute of Technology, Chicago, IL, USA.
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