Physiological variations in people
with type 1 diabetes constantly
change the insulin requirements of patients which, if not compensated,
can lead to insulin overdose or insulin insufficiency causing hypoglycemia
and hyperglycemia episodes, respectively. Here, an offset-free zone
model predictive control (ZMPC) strategy with artificial variables
that automatically adjust its penalty parameters is developed by means
of new adaptation rules to reduce both types of episodes. The online
adaptive tuning is carried out according to the estimation of the
plant–model mismatch, the blood glucose value, and its rate
of change, producing an aggressive or conservative action depending
on the actual situation. In addition, the MPC formulation considers
the input of insulin as an impulse instead of a discrete one. The
developed method is evaluated in 30 virtual patients of the UVA/Padova
simulator and it is compared with an offset-free ZMPC without the
adaptation rule. A significant reduction of hypoglycemia episodes
is obtained and, for adults, adolescents, and children, a time in
normoglycemia range of 87.0%, 67.9%, and 66.1%, respectively, is achieved
in a simulation scenario without meal announcement, 30% of parameter
variations (simultaneously in several parameters), and sensor noise.
The proposed method shows the potential of using information about
the estimated mismatch for the MPC tuning rules to compensate the
physiological variations. This without requiring complex modifications
of the MPC formulation.
Oncolytic virus therapy aims to eradicate tumours using viruses which only infect and destroy targeted tumour cells. It is urgent to improve understanding and outcomes of this promising cancer treatment because oncolytic virus therapy could provide sensible solutions for many patients with cancer. Recently, mathematical modelling of oncolytic virus therapy was used to study different treatment protocols for treating breast cancer cells with genetically engineered adenoviruses. Indeed, it is currently challenging to elucidate the number, the schedule, and the dosage of viral injections to achieve tumour regression at a desired level and within a desired time frame. Here, we apply control theory to this model to advance the analysis of oncolytic virus therapy. The control analysis of the model suggests that at least three viral injections are required to control and reduce the tumour from any initial size to a therapeutic target. In addition, we present an impulsive control strategy with an integral action and a state feedback control which achieves tumour regression for different schedule of injections. When oncolytic virus therapy is evaluated
in silico
using this feedback control of the tumour, the controller automatically tunes the dose of viral injections to improve tumour regression and to provide some robustness to uncertainty in biological rates. Feedback control shows the potential to deliver efficient and personalized dose of viral injections to achieve tumour regression better than the ones obtained by former protocols. The control strategy has been evaluated
in silico
with parameters that represent five nude mice from a previous experimental work. Together, our findings suggest theoretical and practical benefits by applying control theory to oncolytic virus therapy.
Type 1 diabetic patients need a strict treatment to regulate blood glucose concentration in a target range. Despite the development of different control strategies, the model parameter variations, given by physiological changes, can generate an inaccurate treatment and in consequence hyperglycemia and hypoglycemia episodes. Therefore, it is necessary to use control techniques that compensate such effects and maintain the control goals. Here, the effect of parametric variations is examined by the sensitivity analysis from which the most influential parameters in glycemia dynamics are detected. Based on that, an offset-free MPC strategy for impulsive systems is given for the first time in literature and simulated for type 1 diabetes treatment. This scheme along with the impulsive zone MPC with artificial variables reestablishes the normoglycemia behavior since the parameter variations are adequately rejected. However, only parametric variations up to 50% from their nominal values are well compensated, which suggests that more robust formulations are needed to ensure a greater rejection of physiological variations.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.