Anirudh Nath

Bio: Anirudh Nath is an academic researcher from National Institute of Technology, Silchar. The author has contributed to research in topic(s): Observer (quantum physics) & Control theory. The author has an hindex of 7, co-authored 18 publication(s) receiving 162 citation(s). Previous affiliations of Anirudh Nath include Indian Institute of Technology Delhi & University of Calcutta.

Blood glucose regulation in type 1 diabetic patients: an adaptive parametric compensation control-based approach

Abstract: Here, a direct adaptive control strategy with parametric compensation is adopted for an uncertain non-linear model representing blood glucose regulation in type 1 diabetes mellitus patients. The uncertain parameters of the model are updated by appropriate design of adaptation laws using the Lyapunov method. The closed-loop response of the plasma glucose concentration as well as external insulin infusion rate is analysed for a wide range of variation of the model parameters through extensive simulation studies. The result indicates that the proposed adaptive control scheme avoids severe hypoglycaemia and gives satisfactory performance under parametric uncertainty highlighting its ability to address the issue of inter-patient variability.

Observer based nonlinear control design for glucose regulation in type 1 diabetic patients: An LMI approach

Abstract: This paper deals with the design of observer-based nonlinear control of blood glucose concentration (BGC) of Type 1 diabetes mellitus (T1DM) patients in a Linear Matrix Inequality (LMI) framework. The controller design relies on the information of the states obtained from a nonlinear observer. The control law is derived using feedback linearisation and regional pole placement technique. Further, a numerical optimisation method is proposed for the computation of the controller gains by capturing the relationship between transformed domain and original domain dynamics by iteratively tuning the circular LMI region parameters (‘q’ and ‘r’) such that the locations of closed-loop poles of the original nonlinear system are attained. The proposed controller can deliver robust closed-loop response of BGC within a specified range of parametric uncertainty and meal disturbances owing to the appropriately tuned bound of LMI region parameters. The performance of the proposed controller is tested for 100 virtual T1DM patients in the presence of parametric uncertainty and uncertain meal disturbance. Both severe hypoglycemia (

An augmented subcutaneous type 1 diabetic patient modelling and design of adaptive glucose control

Abstract: In the present work, an augmented subcutaneous (SC) model of type 1 diabetic patients (T1DP) is proposed first by estimating the model parameters with the aid of nonlinear least square method using the physiological data. Next, a nonlinear adaptive controller is proposed to tackle two important issues of intra-patient variability (IPV) and uncertain meal disturbance (MD). The proposed patient model agrees quite well with the responses of one of the most popular existing nonlinear model used in the research of artificial pancreas. Further, the developed adaptive control is shown to be capable of providing desired glycemic control without feed-forward action for meal compensation or safety algorithms to avoid hypoglycemia. Due to the simple structure and capability of handling intra-patient variability of the adaptive controller, it can find immediate applicability in the development of the in-silico artificial pancreas.

Physiological Models and Control for Type 1 Diabetes Mellitus: A Brief Review

Abstract: The regulation of blood glucose in Type-1 Diabetes Mellitus (T1DM) patient is being extensively investigated by researchers. This research, therefore, has contributed to the development of many glucose-insulin mathematical models, which at some level successfully mimic the physiological behaviour of the human body. These mathematical models describe the dynamics of glucose in the presence of insulin in the body. One the most important factors affecting the glucose-insulin dynamics is meal intake. Hence, augmenting glucose-insulin dynamics with meal dynamics is very important. Augmented mathematical models have many patient specific physiological parameters which are difficult to estimate. Thus, there is a need to check for the identifiability of the parameters and hence there is a need to identify and estimate these parameters. The most fundamental research, which goes into automation of insulin infusion into the T1DM patient is the development of control algorithms. In this paper, a brief review on some of the important augmented models, identification, parameter estimation and existing control algorithms are presented. For the development of Artificial Pancreas System (APS) integration of glucose sensor is an important issue which in turn introduces sensor noise in the measurement, thereby leading to model imperfection. This paper further discusses the design of control algorithms in the presence of such noises and various other disturbances.

Robust observer-based adaptive control of blood glucose in diabetic patients

Abstract: The objective of this paper is to design a robust observer-based adaptive controller for an intravenous glucose tolerance test (IVGTT) model of Type 1 Diabetes Mellitus (T1DM) patients. The model i...

Adaptive Control

Abstract: This book, written by two major figures in adaptive control, provides a wealth of material for researchers, practitioners, and students. While some researchers in adaptive control may note the absence of a particular topic, the book‘s scope represents a high-gain instrument. It can be used by designers of control systems to enhance their work through the information on many new theoretical developments, and can be used by mathematical control theory specialists to adapt their research to practical needs. The book is strongly recommended to anyone interested in adaptive control.

Observer based nonlinear control design for glucose regulation in type 1 diabetic patients: An LMI approach

Abstract: This paper deals with the design of observer-based nonlinear control of blood glucose concentration (BGC) of Type 1 diabetes mellitus (T1DM) patients in a Linear Matrix Inequality (LMI) framework. The controller design relies on the information of the states obtained from a nonlinear observer. The control law is derived using feedback linearisation and regional pole placement technique. Further, a numerical optimisation method is proposed for the computation of the controller gains by capturing the relationship between transformed domain and original domain dynamics by iteratively tuning the circular LMI region parameters (‘q’ and ‘r’) such that the locations of closed-loop poles of the original nonlinear system are attained. The proposed controller can deliver robust closed-loop response of BGC within a specified range of parametric uncertainty and meal disturbances owing to the appropriately tuned bound of LMI region parameters. The performance of the proposed controller is tested for 100 virtual T1DM patients in the presence of parametric uncertainty and uncertain meal disturbance. Both severe hypoglycemia (

Optimal design of clinical tests for the identification of physiological models of type 1 diabetes mellitus in the presence of model uncertainty

Abstract: Type 1 diabetes mellitus is a disease affecting millions of people worldwide and causing the expenditure of millions of euros every year for health care. One of the most promising therapies derives from the use of an artificial pancreas, based on a control system able to maintain the normoglycaemia in the subject affected by diabetes. A dynamic simulation model of the glucose-insulin system can be useful in several circumstances for diabetes care, including testing of glucose sensors, insulin infusion algorithms, and decision support systems for diabetes. This paper considers the problem of the identification of single individual parameters in detailed dynamic models of glucose homeostasis. Optimal model-based design of experiment techniques are used to design a set of clinical tests that allow the model parameters to be estimated in a statistically sound way, while meeting constraints related to safety of the subject and ease of implementation. The model with the estimated set of parameters represents a specific subject and can thus be used for customized diabetes care solutions. Simulated results demonstrate how such an approach can improve the effectiveness of clinical tests and serve as a tool to devise safer and more efficient clinical protocols, thus providing a contribution to the development of an artificial pancreas.

An augmented subcutaneous type 1 diabetic patient modelling and design of adaptive glucose control

Abstract: In the present work, an augmented subcutaneous (SC) model of type 1 diabetic patients (T1DP) is proposed first by estimating the model parameters with the aid of nonlinear least square method using the physiological data. Next, a nonlinear adaptive controller is proposed to tackle two important issues of intra-patient variability (IPV) and uncertain meal disturbance (MD). The proposed patient model agrees quite well with the responses of one of the most popular existing nonlinear model used in the research of artificial pancreas. Further, the developed adaptive control is shown to be capable of providing desired glycemic control without feed-forward action for meal compensation or safety algorithms to avoid hypoglycemia. Due to the simple structure and capability of handling intra-patient variability of the adaptive controller, it can find immediate applicability in the development of the in-silico artificial pancreas.

Fuzzy PD Plus I Control-based Adaptive Cruise Control System in Simulation and Real-time Environment

Abstract: An effort is made to design the fuzzy proportional-derivative (PD) plus I controller for a nonlinear cruise control system in automobiles, which provides adaptive capability in set-point tracking p...