Data-driven Subspace Approach to Predictive Control

2008 ◽  
pp. 121-141 ◽  
Author(s):  
Biao Huang ◽  
Ramesh Kadali
Keyword(s):  
Predictive Control ◽  
Data Driven

A Nonlinear Predictive Control Approach for Urban Drainage Networks Using Data-Driven Models and Moving Horizon Estimation

2022 ◽  
pp. 1-16
Author(s):  
Krisztian Mark Balla ◽  
Christian Schou ◽  
Jan Dimon Bendtsen ◽  
Carlos Ocampo-Martinez ◽  
Carsten Skovmose Kallesoe
Keyword(s):  
Predictive Control ◽  
Data Driven ◽  
Urban Drainage ◽  
Moving Horizon Estimation ◽  
Control Approach ◽  
Nonlinear Predictive Control ◽  
Drainage Networks ◽  
Using Data

Data Driven Model Predictive Control Design to Enhance Tracking Accuracy and Power Quality of a Solar Based Microgrid under Uncertain Load

2021 ◽  
Author(s):  
Merina Islam ◽  
Sumaiya Yeasmin ◽  
Shahriar Fahim ◽  
Subrata Sarker ◽  
Faisal Badal ◽  
...  
Keyword(s):  
Model Predictive Control ◽  
Power Quality ◽  
Predictive Control ◽  
Control Design ◽  
Data Driven ◽  
Tracking Accuracy

Data-driven model predictive control for building climate control: Three case studies on different buildings

2019 ◽  
Vol 160 ◽  
pp. 106204 ◽  
Author(s):  
Jiangyu Wang ◽  
Shuai Li ◽  
Huanxin Chen ◽  
Yue Yuan ◽  
Yao Huang
Keyword(s):  
Model Predictive Control ◽  
Case Studies ◽  
Predictive Control ◽  
Data Driven ◽  
Climate Control

scholarly journals Data-Driven Control Techniques for Renewable Energy Conversion Systems: Wind Turbine and Hydroelectric Plants

Electronics ◽  
2019 ◽  
Vol 8 (2) ◽  
pp. 237 ◽  
Author(s):  
Silvio Simani ◽  
Stefano Alvisi ◽  
Mauro Venturini
Keyword(s):  
Renewable Energy ◽  
Model Predictive Control ◽  
Energy Conversion ◽  
Wind Turbine ◽  
Predictive Control ◽  
Control Strategies ◽  
Data Driven ◽  
Control Techniques ◽  
Energy Conversion Systems ◽  
Hydroelectric Plants

The interest in the use of renewable energy resources is increasing, especially towards wind and hydro powers, which should be efficiently converted into electric energy via suitable technology tools. To this end, data-driven control techniques represent viable strategies that can be employed for this purpose, due to the features of these nonlinear dynamic processes of working over a wide range of operating conditions, driven by stochastic inputs, excitations and disturbances. Therefore, the paper aims at providing some guidelines on the design and the application of different data-driven control strategies to a wind turbine benchmark and a hydroelectric simulator. They rely on self-tuning PID, fuzzy logic, adaptive and model predictive control methodologies. Some of the considered methods, such as fuzzy and adaptive controllers, were successfully verified on wind turbine systems, and similar advantages may thus derive from their appropriate implementation and application to hydroelectric plants. These issues represent the key features of the work, which provides some details of the implementation of the proposed control strategies to these energy conversion systems. The simulations will highlight that the fuzzy regulators are able to provide good tracking capabilities, which are outperformed by adaptive and model predictive control schemes. The working conditions of the considered processes will be also taken into account in order to highlight the reliability and robustness characteristics of the developed control strategies, especially interesting for remote and relatively inaccessible location of many plants.

Data-Driven Multiobjective Predictive Control for Wastewater Treatment Process

2020 ◽  
Vol 16 (4) ◽  
pp. 2767-2775 ◽  
Author(s):  
Honggui Han ◽  
Zheng Liu ◽  
Ying Hou ◽  
Junfei Qiao
Keyword(s):  
Wastewater Treatment ◽  
Predictive Control ◽  
Treatment Process ◽  
Data Driven ◽  
Wastewater Treatment Process
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