scholarly journals Reducing usage of the computational resources by event driven approach to model predictive control

2017 ◽  
Vol 68 (4) ◽  
pp. 290-298
Author(s):  
Stefan Misik ◽  
Zdenek Bradac ◽  
Arben Cela
Keyword(s):  
Model Predictive Control ◽  
Real Time ◽  
Predictive Control ◽  
Dynamic Systems ◽  
Real Time Systems ◽  
Simple Modification ◽  
Control Approach ◽  
Simulated Environment ◽  
Computational Resources ◽  
Time Systems

Abstract This paper deals with a real-time and optimal control of dynamic systems while also considers the constraints which these systems might be subject to. Main objective of this work is to propose a simple modification of the existing Model Predictive Control approach to better suit needs of computational resource-constrained real-time systems. An example using model of a mechanical system is presented and the performance of the proposed method is evaluated in a simulated environment.

scholarly journals Real-Time Model Predictive Control of Human Bodyweight Based on Energy Intake

2019 ◽  
Vol 9 (13) ◽  
pp. 2609 ◽  
Author(s):  
Peña Fernández ◽  
Youssef ◽  
Heeren ◽  
Matthys ◽  
Aerts
Keyword(s):  
Body Weight ◽  
Model Predictive Control ◽  
Real Time ◽  
Energy Intake ◽  
Predictive Control ◽  
Reference Trajectory ◽  
Time Model ◽  
Control Approach ◽  
Overweight People ◽  
Mean Square Errors

The number of overweight people reached 1.9 billion in 2016. Lifespan decrease and many diseases have been linked to obesity. Efficient ways to monitor and control body weight are needed. The objective of this work is to explore the use of a model predictive control approach to manage bodyweight in response to energy intake. The analysis is performed based on data obtained during the Minnesota starvation experiment, with weekly measurements on body weight and energy intake for 32 male participants over the course of 27 weeks. A first order dynamic auto-regression with exogenous variables model exhibits the best prediction, with an average mean relative prediction error value of 1.01 ± 0.02% for 1 week-ahead predictions. Then, the performance of a model predictive control algorithm, following a predefined bodyweight trajectory, is tested. Root mean square errors of 0.30 ± 0.06 kg and 9 ± 3 kcal day-1 are found between the desired target and simulated bodyweights, and between the measured energy intake and advised by the controller energy intake, respectively. The model predictive control approach for bodyweight allows calculating the needed energy intake in order to follow a predefined target bodyweight reference trajectory. This study shows a first possible step towards real-time active control of human bodyweight.

Sign in / Sign up

Export Citation Format

Share Document