Model predictive control of a large fleet of thermal loads and electric power generators, with an assessment for the Netherlands

2013 ◽  
Author(s):  
Gregory Ledva ◽  
Robin Vujanic ◽  
Sebastien Mariethoz ◽  
Manfred Morari ◽  
Jasper Frunt
Keyword(s):  
The Netherlands ◽  
Model Predictive Control ◽  
Electric Power ◽  
Predictive Control ◽  
Thermal Loads ◽  
Power Generators

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.

Multi-parametric model predictive control for autonomous steering using an electric power steering system

2019 ◽  
Vol 233 (13) ◽  
pp. 3391-3402
Author(s):  
Junho Lee ◽  
Hyuk-Jun Chang
Keyword(s):  
Model Predictive Control ◽  
Quadratic Programming ◽  
Electric Power ◽  
Predictive Control ◽  
Parametric Model ◽  
Effective Control ◽  
Steering Control ◽  
Electric Power Steering ◽  
Parametric Quadratic Programming ◽  
Power Steering

Electric power steering systems have been used to generate assist torque for driver comfort. This study makes use of the functionality of electric power steering systems for autonomous steering control without driver torque. A column-type electric power steering test bench, equipped with a brushless DC motor as an assist motor, and the Infineon TriCore AURIX TC 277 microcontroller was used in this study. Multi-parametric model predictive control is based on a model predictive control–based approach that employs a multi-parametric quadratic programming technique. This technique allows the reduction of the huge computational burden resulting from the online optimization in model predictive control. The proposed controller obtains an optimal input based on multi-parametric quadratic programming at each sampling time. The weighting matrix definition, which is the main task when designing the proposed controller, was analyzed. The experimental results of the step response of the steering wheel angle verified the tracking ability of the proposed controller for different ranges of the prediction horizon. Since the computational loads are directly related to functional safety, the results of this study support the use of the multi-parametric model predictive control scheme as an effective control method for autonomous steering control.

Design of a Parameterized Model Predictive Control for Electric Power Assisted Steering

2019 ◽  
Vol 90 ◽  
pp. 331-341
Author(s):  
André Murilo ◽  
Rafael Rodrigues ◽  
Evandro Leonardo Silva Teixeira ◽  
Max Mauro Dias Santos
Keyword(s):  
Model Predictive Control ◽  
Electric Power ◽  
Predictive Control ◽  
Parameterized Model
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