scholarly journals Time-distributed optimization for real-time model predictive control: Stability, robustness, and constraint satisfaction

Automatica ◽  
2020 ◽  
Vol 117 ◽  
pp. 108973 ◽  
Author(s):  
Dominic Liao-McPherson ◽  
Marco M. Nicotra ◽  
Ilya Kolmanovsky
Keyword(s):  
Model Predictive Control ◽  
Real Time ◽  
Predictive Control ◽  
Constraint Satisfaction ◽  
Distributed Optimization ◽  
Time Model ◽  
Stability Robustness ◽  
Control Stability

scholarly journals Real-Time Model Predictive Control of Rigid Body Motion via Discretization Using the Cayley Map

IEEE Access ◽  
2020 ◽  
Vol 8 ◽  
pp. 17149-17159 ◽  
Author(s):  
Yuichi Tadokoro ◽  
Yuki Taya ◽  
Tatsuya Ibuki ◽  
Mitsuji Sampei
Keyword(s):  
Model Predictive Control ◽  
Rigid Body ◽  
Real Time ◽  
Predictive Control ◽  
Rigid Body Motion ◽  
Body Motion ◽  
Time Model ◽  
Cayley Map

Real-Time Model Predictive Control for Shipboard Power Management Using the IPA-SQP Approach

2015 ◽  
Vol 23 (6) ◽  
pp. 2129-2143 ◽  
Author(s):  
Hyeongjun Park ◽  
Jing Sun ◽  
Steven Pekarek ◽  
Philip Stone ◽  
Daniel Opila ◽  
...  
Keyword(s):  
Model Predictive Control ◽  
Real Time ◽  
Power Management ◽  
Predictive Control ◽  
Time Model ◽  
Shipboard Power

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