Anirudh Nath

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

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...

Robust observer based control for plasma glucose regulation in type 1 diabetes patient using attractive ellipsoid method.

Abstract: This paper deals with the design of robust observer based output feedback control law for the stabilisation of an uncertain nonlinear system and subsequently apply the developed method for the regulation of plasma glucose concentration in Type 1 diabetes (T1D) patients. The principal objective behind the proposed design is to deal with the issues of intra-patient parametric variation and non-availability of all state variables for measurement. The proposed control technique for the T1D patient model is based on the attractive ellipsoid method (AEM). The observer and controller conditions are obtained in terms of linear matrix inequality (LMI), thus allowing to compute easily both the observer and controller gains. The closed-loop response obtained using the designed controller avoids adverse situations of hypoglycemia and post-prandial hyperglycemia under uncertain conditions. Further to validate the robustness of the design, closed-loop simulations of random 200 virtual T1D patients considering parameters within the considered ranges are presented. The results indicate that hypoglycemia and post-prandial hyperglycemia are significantly reduced in the presence of bounded ( ± 30 % ) parametric variability and uncertain exogenous meal disturbance.

Closed Loop Blood Glucose Regulation of Type 1 Diabetic Patient Using Takagi-Sugeno Fuzzy Logic Control

Abstract: This paper presents a Takagi-Sugeno (TS) Fuzzy Logic based intelligent control algorithm for maintaining plasma glucose regulation for Type 1 Diabetic Mellitus (T1DM) patient in presence of known meal disturbance and parametric variations. The mathematical model considered here to describe the interaction between glucose and insulin, is modified Bergman minimal model. Simulation results of the plasma glucose concentration and insulin infusion rate validate the effectiveness of the designed control law.

Glucose Regulation in Diabetes Patients Via Insulin Pump: A Feedback Linearisation Approach

Abstract: The primary objective of the paper is to design a nonlinear control technique for a nonlinear intravenous model of Type 1 diabetes mellitus (T1DM) patient. Input–output feedback linearisation is utilised for deriving the nonlinear control law based on a modified version of Bergman’s minimal model augmented with the dynamics of the insulin pump and the meal disturbance. The results depict that the proposed control technique avoids severe hypoglycaemia and postprandial hyperglycaemia in the presence of exogenous meal disturbance as well as parametric uncertainty within a population of 100 virtual T1DM patients (inter-patient variability). The efficacy of the proposed control technique is investigated through variability grid analysis.

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.

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.

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...