Diabetes is known as the sixth leading cause of death in the world leading to kidney and cardiovascular diseases as well. As a result, controlling the disease is of particular importance. To control blood glucose leve...
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ISBN:
(纸本)9781467365062
Diabetes is known as the sixth leading cause of death in the world leading to kidney and cardiovascular diseases as well. As a result, controlling the disease is of particular importance. To control blood glucose level in diabetes mellitus type I, insulin must be injected into the body. This injection must be strictly controlled any excessive increase in insulin injection can cause human death. Various controllers have been introduced to control diabetes type I with their own disadvantages. Diabetic patient model used in previous methods is not in compliance with physiological data, uncertainties are not considered in the model, or the controller has a very limited range of stability. In this paper, a nonlinear model predictive controller is introduced which not only resolves previous problems like lack of consideration of model uncertainty, sharp drop in blood glucose, and low stability, but also specifies insulin injection dosage based on blood glucose level momentarily so that.
This paper analyzes the prediction model of a nonlinearmodelpredictive formation controller (NMPFC) applied to control the formation of a team of omnidirectional mobile robots. The prediction model calculates future...
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ISBN:
(纸本)9781479967117
This paper analyzes the prediction model of a nonlinearmodelpredictive formation controller (NMPFC) applied to control the formation of a team of omnidirectional mobile robots. The prediction model calculates future formation behaviors with respect to obstacles, teammates in formation, target, orientation, position in formation and control effort using a kinematic model to predict most of the formation terms. Nevertheless, the prediction of the robots in formation can be done by either dynamic models or pure kinematic models. Therefore, this paper presents an analysis in order to find a balanced robot's prediction model that stimulates the robots the converge in less time and minimizing the controller's cost function. Finally, results of experiments with simulated robots are presented and discussed.
State estimation and estimator based predictive control of nonlinear autonomous hybrid systems poses a challenging problem as these systems involve discontinuities that are introduced by switching of the discrete vari...
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State estimation and estimator based predictive control of nonlinear autonomous hybrid systems poses a challenging problem as these systems involve discontinuities that are introduced by switching of the discrete variables. In this paper, we propose a state estimation scheme for an autonomous hybrid system using an ensemble Kalman filter (EnKF), which belongs to the class of particle filters and is a derivative free nonlinear state estimator. We then proceed to develop a novel nonlinearmodelpredictive control scheme that inherits the approach used in EnKF formulation for future trajectory predictions. The efficacy of the proposed state estimation and control scheme is demonstrated by conducting simulation studies on a benchmark hybrid three-tank system.
In this work, we develop a state estimation scheme for nonlinear autonomous hybrid systems, which are subjected to stochastic state disturbances and measurement noise, using derivative free state estimators. In partic...
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In this work, we develop a state estimation scheme for nonlinear autonomous hybrid systems, which are subjected to stochastic state disturbances and measurement noise, using derivative free state estimators. In particular, we propose the use of ensemble Kalman filters (EnKF), which belong to the class of particle filters, and unscented Kalman filters (UKF) to carry out estimation of state variables of autonomous hybrid system. We then proceed to develop novel nonlinearmodelpredictive control (NMPC) schemes using these derivative free estimators for better control of autonomous hybrid systems. A salient feature of the proposed NMPC schemes is that the future trajectory predictions are based on stochastic simulations, which explicitly account for the uncertainty in predictions arising from the uncertainties in the initial state and the unmeasured disturbances. The efficacy of the proposed state estimation based control scheme is demonstrated by conducting simulation studies on a benchmark three-tank hybrid system. Analysis of the simulation results reveals that EnKF and UKF based NMPC strategies is well suited for effective control of nonlinear autonomous three-tank hybrid system. (C) 2010 Elsevier Ltd. All rights reserved.
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