Experiment study of machine-learning-based approximate model predictive control for energy-efficient building control

2021 ◽  
Vol 288 ◽  
pp. 116648 ◽  
Author(s):  
Shiyu Yang ◽  
Man Pun Wan ◽  
Wanyu Chen ◽  
Bing Feng Ng ◽  
Swapnil Dubey
Keyword(s):  
Machine Learning ◽  
Model Predictive Control ◽  
Predictive Control ◽  
Energy Efficient ◽  
Approximate Model ◽  
Experiment Study ◽  
Building Control ◽  
Energy Efficient Building

scholarly journals Use of model predictive control and weather forecasts for energy efficient building climate control

2012 ◽  
Vol 45 ◽  
pp. 15-27 ◽  
Author(s):  
Frauke Oldewurtel ◽  
Alessandra Parisio ◽  
Colin N. Jones ◽  
Dimitrios Gyalistras ◽  
Markus Gwerder ◽  
...  
Keyword(s):  
Model Predictive Control ◽  
Predictive Control ◽  
Energy Efficient ◽  
Climate Control ◽  
Weather Forecasts ◽  
Energy Efficient Building

scholarly journals Knee-stretched walking with toe-off and heel-strike for a position-controlled humanoid robot based on model predictive control

2021 ◽  
Vol 18 (4) ◽  
pp. 172988142110362
Author(s):  
Zelin Huang ◽  
Zhangguo Yu ◽  
Xuechao Chen ◽  
Qingqing Li ◽  
Libo Meng ◽  
...  
Keyword(s):  
Model Predictive Control ◽  
Predictive Control ◽  
Energy Efficient ◽  
Humanoid Robot ◽  
Center Of Mass ◽  
Heel Strike ◽  
Gait Generation ◽  
Double Support ◽  
Transition Moments ◽  
Efficient Gait

Knee-stretched walking is considered to be a human-like and energy-efficient gait. The strategy of extending legs to obtain vertical center of mass trajectory is commonly used to avoid the problem of singularities in knee-stretched gait generation. However, knee-stretched gait generation utilizing this strategy with toe-off and heel-strike has kinematics conflicts at transition moments between single support and double support phases. In this article, a knee-stretched walking generation with toe-off and heel-strike for the position-controlled humanoid robot has been proposed. The position constraints of center of mass have been considered in the gait generation to avoid the kinematics conflicts based on model predictive control. The method has been verified in simulation and validated in experiment.

Machine Learning Emulation of Model Predictive Control for Modular Multilevel Converters

2020 ◽  
pp. 1-1
Author(s):  
Songda Wang ◽  
Tomislav Dragicevic ◽  
Gustavo Figueiredo Gontijo ◽  
Sanjay K. Chaudhary ◽  
Remus Teodorescu
Keyword(s):  
Machine Learning ◽  
Model Predictive Control ◽  
Predictive Control ◽  
Multilevel Converters ◽  
Modular Multilevel Converters
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