Neural-Networks-Based Feedback Linearization versus Model Predictive Control of Continuous Alcoholic Fermentation Process

2005 ◽  
Vol 28 (10) ◽  
pp. 1191-1200 ◽  
Author(s):  
F. S. Mjalli ◽  
S. Al-Asheh
Keyword(s):  
Neural Networks ◽  
Model Predictive Control ◽  
Predictive Control ◽  
Feedback Linearization ◽  
Fermentation Process ◽  
Alcoholic Fermentation ◽  
Versus Model

Nonlinear Model Predictive Control via Feedback Linearization of a Greenhouse

2000 ◽  
Vol 33 (25) ◽  
pp. 191-196 ◽  
Author(s):  
Sandra Piñón ◽  
Miguel Peña ◽  
Carlos Soria ◽  
Benjamín Kuchen
Keyword(s):  
Model Predictive Control ◽  
Predictive Control ◽  
Nonlinear Model ◽  
Feedback Linearization ◽  
Nonlinear Model Predictive Control

scholarly journals A Model Predictive Control Design for Power Generation Heavy-Duty Gas Turbines

Energies ◽  
2019 ◽  
Vol 12 (11) ◽  
pp. 2182 ◽  
Author(s):  
Alessandro Rosini ◽  
Alessandro Palmieri ◽  
Damiano Lanzarotto ◽  
Renato Procopio ◽  
Andrea Bonfiglio
Keyword(s):  
Power Generation ◽  
Model Predictive Control ◽  
Electric Power ◽  
Predictive Control ◽  
Gas Turbines ◽  
Feedback Linearization ◽  
Renewable Energy Sources ◽  
Current Standard ◽  
Power Generators ◽  
Highly Nonlinear

The new electric power generation scenario, characterized by growing variability due to the greater presence of renewable energy sources (RES), requires more restrictive dynamic requirements for conventional power generators. Among traditional power generators, gas turbines (GTs) can regulate the output electric power faster than any other type of plant; therefore, they are of considerable interest in this context. In particular, the dynamic performance of a GT, being a highly nonlinear and complex system, strongly depends on the applied control system. Proportional–integral–derivative (PID) controllers are the current standard for GT control. However, since such controllers have limitations for various reasons, a model predictive control (MPC) was designed in this study to enhance GT performance in terms of dynamic behavior and robustness to model uncertainties. A comparison with traditional PID-based controllers and alternative model-based control approaches (feedback linearization control) found in the literature demonstrated the effectiveness of the proposed approach.

Using Linear Model Predictive Control via Feedback Linearization for dynamic encirclement

2014 ◽  
Author(s):  
A. T. Hafez ◽  
M. Iskandarani ◽  
S. N. Givigi ◽  
S. Yousefi ◽  
Camille Alain Rabbath ◽  
...  
Keyword(s):  
Model Predictive Control ◽  
Linear Model ◽  
Predictive Control ◽  
Feedback Linearization
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