Optimal dosing of cancer chemotherapy using model predictive control and moving horizon state/parameter estimation

2012 ◽  
Vol 108 (3) ◽  
pp. 973-983 ◽  
Author(s):  
Tao Chen ◽  
Norman F. Kirkby ◽  
Raj Jena
Keyword(s):  
Parameter Estimation ◽  
Model Predictive Control ◽  
Cancer Chemotherapy ◽  
Predictive Control ◽  
State Parameter ◽  
Optimal Dosing

Model Predictive Control of a Distributed-Parameter Process Employing a Moving Radiant Actuator

2010 ◽  
Author(s):  
Fan Zeng ◽  
Beshah Ayalew
Keyword(s):  
Model Predictive Control ◽  
Predictive Control ◽  
Process Model ◽  
Energy Use ◽  
State Parameter ◽  
Source Model ◽  
Distributed Parameter ◽  
Optimization Approach ◽  
Radiant Intensity ◽  
Process Energy

Many industrial processes employ radiation-based actuators with two or more manipulated variables. Moving radiant actuators, in particular, act on a distributed parameter process where the velocity of the actuator is an additional manipulated variable with its own constraints. In this paper, a model predictive control (MPC) scheme is developed for a distributed-parameter process employing such a moving radiant actuator. The designed MPC controller uses an online optimization approach to determine both the radiant intensity and velocity of the moving actuator based on a linearized process model and a distributed state/parameter estimator. A particular source-model reduction that enables the approach is outlined. The proposed strategy is then demonstrated for a radiative curing process considering different control scenarios with the objective of achieving desired cure level uniformity and minimizing process energy use.

scholarly journals Biogas production management systems with model predictive control of anaerobic digestion processes

2020 ◽  
Vol 43 (12) ◽  
pp. 2189-2200
Author(s):  
Kazuto Yoshida ◽  
Naoto Shimizu
Keyword(s):  
Parameter Estimation ◽  
Anaerobic Digestion ◽  
Model Predictive Control ◽  
Prediction Model ◽  
Predictive Control ◽  
Management System ◽  
Production Management ◽  
Biogas Production ◽  
Production Management System ◽  
Estimation System

Abstract We developed a biogas production management system to control biogas production by determining the feedstock inputs to the anaerobic digestion process according to fluctuations of the renewable energy supply. The developed system consists of three functions: a prediction model for the anaerobic digestion processes, a parameter-estimation system, and a feedstock-determination controller. A prediction model for the anaerobic digestion processes in a state-space representation was constructed for the input–output relationship of biogas generation from organic compounds and the state of methane fermentation. A parameter-estimation system that estimated the parameters included in the prediction model from actual operating process data was built based on adaptive identification theory. The feedstock-determination controller was established based on model predictive control as a method to control biogas production. From the results of the identification experiment, the least square estimator of the parameters converged as the training data increased, and a reliable parameter was given in 1 week. From the results of the numerical simulation and the control experiment, it was confirmed that the biogas production management system developed in this study had a high prediction accuracy and control performance.

Finite set model predictive control with on-line parameter estimation for active frond-end converters

2017 ◽  
Vol 100 (3) ◽  
pp. 1497-1507 ◽  
Author(s):  
Mohamed Abdelrahem ◽  
Christoph Michael Hackl ◽  
Ralph Kennel
Keyword(s):  
Parameter Estimation ◽  
Model Predictive Control ◽  
Predictive Control ◽  
Line Parameter ◽  
Finite Set ◽  
On Line
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