Embedded Real-Time Model Predictive Control for Glucose Regulation

Chui, Chee-Kong; Nguyen, Binh P.; Ho, Yvonne; Wu, Zimei; Nguyen, Mai Linh; Gao, Hong; Mok, Daniel; Sun, Sumei; Chang, Stephen

doi:10.1007/978-3-642-29305-4_378

Cited by 6 publications

(6 citation statements)

References 10 publications

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“…A microcontroller is suitable for the task if the optimization problem fits into the memory system and if the required computational latency is more than a millisecond. For example, a primal-dual interior point method for the control of an artificial pancreas on a microcontroller is reported in [26].…”

Section: A Processorsmentioning

confidence: 99%

Computer architectures to close the loop in real-time optimization

Kerrigan

Constantinides

Suardi

et al. 2015

2015 54th IEEE Conference on Decision and Control (CDC)

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Abstract-Many modern control, automation, signal processing and machine learning applications rely on solving a sequence of optimization problems, which are updated with measurements of a real system that evolves in time. The solutions of each of these optimization problems are then used to make decisions, which may be followed by changing some parameters of the physical system, thereby resulting in a feedback loop between the computing and the physical system. Real-time optimization is not the same as 'fast' optimization, due to the fact that the computation is affected by an uncertain system that evolves in time. The suitability of a design should therefore not be judged from the optimality of a single optimization problem, but based on the evolution of the entire cyber-physical system. The algorithms and hardware used for solving a single optimization problem in the office might therefore be far from ideal when solving a sequence of real-time optimization problems. Instead of there being a single, optimal design, one has to trade-off a number of objectives, including performance, robustness, energy usage, size and cost. We therefore provide here a tutorial introduction to some of the questions and implementation issues that arise in real-time optimization applications. We will concentrate on some of the decisions that have to be made when designing the computing architecture and algorithm and argue that the choice of one informs the other.

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Section: A Processorsmentioning

confidence: 99%

Computer architectures to close the loop in real-time optimization

Kerrigan

Constantinides

Suardi

et al. 2015

2015 54th IEEE Conference on Decision and Control (CDC)

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“…With these data, we can then develop an accurate glycaemia forecast. Some very successful MPC-based solutions for glycaemia prediction, which make use of microcontrollers, have been presented in the literature [8]. Although time series values-prediction is well-trodden in the field of Machine Learning (ML), it is common to perform appropriate feature selection such as pre-processing, in order to enhance the efficacy of the predictive algorithm.…”

Section: Introductionmentioning

confidence: 99%

A Comparison of Feature Selection and Forecasting Machine Learning Algorithms for Predicting Glycaemia in Type 1 Diabetes Mellitus

et al. 2021

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Type 1 diabetes mellitus (DM1) is a metabolic disease derived from falls in pancreatic insulin production resulting in chronic hyperglycemia. DM1 subjects usually have to undertake a number of assessments of blood glucose levels every day, employing capillary glucometers for the monitoring of blood glucose dynamics. In recent years, advances in technology have allowed for the creation of revolutionary biosensors and continuous glucose monitoring (CGM) techniques. This has enabled the monitoring of a subject’s blood glucose level in real time. On the other hand, few attempts have been made to apply machine learning techniques to predicting glycaemia levels, but dealing with a database containing such a high level of variables is problematic. In this sense, to the best of the authors’ knowledge, the issues of proper feature selection (FS)—the stage before applying predictive algorithms—have not been subject to in-depth discussion and comparison in past research when it comes to forecasting glycaemia. Therefore, in order to assess how a proper FS stage could improve the accuracy of the glycaemia forecasted, this work has developed six FS techniques alongside four predictive algorithms, applying them to a full dataset of biomedical features related to glycaemia. These were harvested through a wide-ranging passive monitoring process involving 25 patients with DM1 in practical real-life scenarios. From the obtained results, we affirm that Random Forest (RF) as both predictive algorithm and FS strategy offers the best average performance (Root Median Square Error, RMSE = 18.54 mg/dL) throughout the 12 considered predictive horizons (up to 60 min in steps of 5 min), showing Support Vector Machines (SVM) to have the best accuracy as a forecasting algorithm when considering, in turn, the average of the six FS techniques applied (RMSE = 20.58 mg/dL).

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“…Most of the research on discrete linearized state-space MPC focused on reducing either the complexity of the quadratic programming (QP) or increasing the speed of the computation of the QP, or both. The existing works on online MPC methods include fast gradient [6,7], active set [8][9][10], interior point [11][12][13][14][15][16], Newton's method [9,17,18], and Hildreth's QP [19], and others [20]. In [21], a faster online MPC was achieved by combing several techniques such as explicit MPC, primal barrier interior point method, warm start, and Newton's method.…”

Section: Introductionmentioning

confidence: 99%

Efficient embedded architectures for fast-charge model predictive controller for battery cell management in electric vehicles

Madsen

Perera

2018

J Embedded Systems

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With the ever-growing concerns about carbon emissions and air pollution throughout the world, electric vehicles (EVs) are one of the most viable options for clean transportation. EVs are typically powered by a battery pack such as lithium-ion, which is created from a large number of individual cells. In order to enhance the durability and prolong the useful life of the battery pack, it is imperative to monitor and control the battery packs at the cell level. Model predictive controller (MPC) is considered as a feasible technique for cell-level monitoring and controlling of the battery packs. For instance, the fast-charge MPC algorithm keeps the Li-ion battery cell within its optimal operating parameters while reducing the charging time. In this case, the fast-charge MPC algorithm should be executed on an embedded platform mounted on an individual cell; however, the existing algorithm for this technique is designed for general-purpose computing. In this research work, we introduce novel, unique, and efficient embedded hardware and software architectures for the fast-charge MPC algorithm, considering the constraints and requirements associated with the embedded devices. We create two unique hardware versions: register-based and memory-based. Experiments are performed to evaluate and illustrate the feasibility and efficiency of our proposed embedded architectures. Our embedded architectures are generic, parameterized, and scalable. Our hardware designs achieved 100 times speedup compared to its software counterparts.

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Embedded Real-Time Model Predictive Control for Glucose Regulation

Cited by 6 publications

References 10 publications

Computer architectures to close the loop in real-time optimization

Computer architectures to close the loop in real-time optimization

A Comparison of Feature Selection and Forecasting Machine Learning Algorithms for Predicting Glycaemia in Type 1 Diabetes Mellitus

Efficient embedded architectures for fast-charge model predictive controller for battery cell management in electric vehicles

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