Pinky Dua

Model-based blood glucose control for type 1 diabetes via parametric programming

An advanced model-based control technique for regulating the blood glucose for patients with Type 1 diabetes is presented. The optimal insulin delivery rate is obtained off-line as an explicit function of the current blood glucose concentration of the patient by using novel parametric programming algorithms, developed at Imperial College London. The implementation of the optimal insulin delivery rate, therefore, requires simple function evaluation and minimal on-line computations. The proposed framework also addresses the uncertainty in the model due to interpatient and intrapatient variability by identifying the model parameters which ensure that a feasible control law can be obtained. The developments reported in this paper are expected to simplify the insulin delivery mechanism, thereby enhancing the quality of life of the patient

Optimal delivery of chemotherapeutic agents in cancer

Cancer refers to a set of diseases where normal cells of the body lose mechanisms which are responsible for controlling their growth and motility. Chemotherapy aims to kill these abnormal or cancer cells, however normal cells and healthy tissues are also adversely affected. Systematic modelling and optimization approaches can be employed to obtain effective chemotherapeutic protocol that minimizes the tumour cell population while limiting the damage to the normal cells. In this paper, two models for cancer chemotherapy are considered and the effect of the time of drug administration on the final tumour size is analyzed. The first model is cell cycle non-specific and considers all the cancer cells as belonging to one compartment whereas the second model is cell cycle specific and takes into account the effect of the drugs that act on cells at certain specific stages of the cell cycle. An optimal control problem is also formulated and solved for both the models so as to obtain the chemotherapeutic schedule which minimizes the final tumour size while taking into account the constraints on drug resistance and toxicity. For the first model a mixed integer dynamic optimization problem and for the second model a dynamic optimization problem are solved. The optimal chemotherapeutic drug administration profiles and the variation of number of cells with time are obtained and analyzed in detail.

MPC on a chip - Recent advances on the application of multi-parametric model-based control

Multi-parametric model-based control (mp-MPC) is a control method that is widely acknowledged for its ability to solve the on-line optimisation problem, involved in traditional MPC, off-line via parametric optimisation. Its main advantage is that it obtains the control actions as explicit functions of the plant measurements. This allows for the control actions to be obtained on-line via simple function evaluations instead of solving repetitively a computationally demanding on-line optimisation. This allows mp-MPC to be implemented on the simplest, low-cost hardware. In this paper we report on recent developments on industrial and experimental applications of mp-MPC, where the ability of mp-MPC to be applied on systems with fast dynamics and sampling times is demonstrated, which maybe prohibitive for traditional MPC that relies on on-line optimisation methods.

Modelling and control of drug delivery systems

Abstract This work presents a compartmental model for delivery of drugs under anesthesia and an advanced model based control algorithm for insulin delivery for Type 1 diabetes. The model for anesthesia involves choice of three drugs isoflurane, dopamine and sodium nitroprusside, which allows simultaneous regulation of mean arterial pressure and unconsciousness of the patients. A number of dynamic simulations are carried out to validate the model. For Type 1 diabetes, a parametric programming approach is used to obtain the optimal insulin infusion rate as an explicit function of the state of the patient and the regions in the space of the state of patient where these functions are valid. These explicit functions allow the implementation of blood glucose control on a simple computational software and hardware platform.

Optimal Delivery of Chemotherapeutic Agents in Cancer

In this paper, derivation of the optimal chemotherapy schedule is formulated and solved as a dynamic optimization problem. For this purpose two models representing the tumour growth kinetics are considered. The dynamic optimization problem for the first model, which is cell cycle non-specific, takes into account multiple time characteristics, drug resistance and toxicity. The discontinuity in the model is formulated by introducing integer variables. For the second model, which is cell cycle specific, the tumour growth is modelled via two compartments: proliferating and resting compartment.

Multi-objective Parametric Control of Blood Glucose Concentration for Type 1 Diabetes

The main objective of automatic blood glucose concentration controllers is to avoid hypo- and hyper-glycemia, i.e. maintain the concentration levels between 60 and 120 mg/dL, by optimally manipulating the insulin infusion rates. In the medical literature, hypoglycemia is considered to be more harmful to an individuals health than hyperglycemia. In this work, a multiobjective controller that can take into account this asymmetry in the objective function and can be implemented with minimal computational effort, by using parametric programming techniques, is proposed. An alternative technique that gives higher priority to the satisfaction of constraints on hypoglycemia than on hyperglycemia is also presented. The performance of both the controllers is analyzed under meal disturbances.

Model based control for insulin delivery for type 1 diabetics via parametric programming

This paper presents an advanced model based control technique for regulating the blood glucose in Type 1 diabetic patients based upon novel parametric programming algorithms, developed at Imperial College London. The optimal insulin delivery rate is obtained off-line as an explicit function of the current blood glucose level of the patient. The implementation of the optimal insulin delivery, therefore, requires simple function evaluation and minimal on-line computations. This is expected to simplify the drug delivery mechanism, thereby enhancing the quality of life of the patients.

Model based parametric control in anesthesia

Abstract This work presents a compartmental model for delivery of three drugs (isoflurane, dopamine and sodium nitroprusside) for regulation of anesthesia. The key feature of this model is that mean arterial pressure, cardiac output and unconsciousness of the patient can be simultaneously regulated. This model is ‘validated’ by carrying out a number of dynamic state simulations and then used for designing model based parametric controllers.

Model based parametric controller for the operation of an experimental reactor

Abstract The aim of this work is to demonstrate on a pilot-plant-scale partially simulated exothermic reactor the implementation and performance of novel parametric controllers, recently developed at Imperial College London. A first-principles model of the process is used and then the parametric controller for the plant model is derived. The controller is given by a set of piecewise affine functions of the manipulating variables, the reactor input flow and cooling jacket temperature, in terms of the controlled variables, the temperature and concentration of the reactor. These affine functions are stored on a computer, which is interfaced to the plant using PARAGON, 5.3. The on-line model-based control therefore reduces to simple affine function evaluations.

MODEL BASED DRUG DELIVERY FOR ANESTHESIA

Abstract In this work a compartmental model for delivery of three drugs (isoflurane, dopamine and sodium nitroprusside) for anesthesia is presented. The key feature of this model is that mean arterial pressure and unconsciousness of the patient can be simultaneously regulated. A number of dynamic simulation experiments are carried out for the validation of the model.