Integration of a predictive control model and MPPT for PV station

2016 ◽  
Author(s):  
Imad Elzein ◽  
Yury N. Petrenko
Keyword(s):  
Predictive Control ◽  
Control Model

scholarly journals Optimal Harvesting Effort for Nonlinear Predictive Control Model for a Single Species Fishery

2015 ◽  
Vol 2015 ◽  
pp. 1-8 ◽  
Author(s):  
Messaoud Bounkhel ◽  
Lotfi Tadj
Keyword(s):  
Predictive Control ◽  
Renewable Resource ◽  
Single Species ◽  
State Equation ◽  
Control Model ◽  
Optimal Harvesting ◽  
Solution Approach ◽  
Nonlinear Predictive Control ◽  
Nonlinear State ◽  
Resource System

We use nonlinear model predictive control to find the optimal harvesting effort of a renewable resource system with a nonlinear state equation that maximizes a nonlinear profit function. A solution approach is proposed and discussed and satisfactory numerical illustrations are provided.

Optimal predictive control model of wind turbine

2021 ◽  
Author(s):  
Soukaina Bougdour ◽  
Rime Elhouti ◽  
Selma Sefriti ◽  
Ismail Boumhidi
Keyword(s):  
Wind Turbine ◽  
Predictive Control ◽  
Control Model ◽  
Optimal Predictive Control

scholarly journals An Assessment of WEC Control Performance Uncertainty

2017 ◽  
Author(s):  
Ryan G. Coe ◽  
Giorgio Bacelli ◽  
Ossama Abdelkhalik ◽  
David G. Wilson
Keyword(s):  
Dynamic Model ◽  
Predictive Control ◽  
Control Strategy ◽  
Control Model ◽  
Control Performance ◽  
Energy Converter ◽  
Model Accuracy ◽  
Linear Dynamic ◽  
Low Sensitivity ◽  
Performance Uncertainty

A linear dynamic model for a wave energy converter (WEC) has been developed based on the results of experimental wave tank testing. Based on this model, a model predictive control (MPC) strategy has been designed and implemented. To assess the performance of this control strategy, a deployment environment off the coast of Newport, OR has been selected and the controller has been used to simulate the WEC response in a set of irregular sea states. To better understand the influence of model accuracy on control performance, an uncertainty analysis has been performed by varying the parameters of the model used for the design of the controller (i.e. the control model), while keeping the WEC dynamic model employed in these simulations (i.e. the plant model) unaltered. The results of this study indicate a relative low sensitivity of the MPC control strategy to uncertainties in the controller model for the specific case studied here.

scholarly journals Discussion on Muskingum versus Integrator-Delay Models for Control Objectives

2014 ◽  
Vol 2014 ◽  
pp. 1-11 ◽  
Author(s):  
Yolanda Bolea ◽  
Vicenç Puig ◽  
Antoni Grau
Keyword(s):  
Comparative Study ◽  
Predictive Control ◽  
Scientific Community ◽  
Hydrological Model ◽  
Control Design ◽  
Control Model ◽  
Irrigation District ◽  
Irrigation Canal ◽  
Delay Models ◽  
Control Community

A comparative study about two models, Muskingum and integrator-delay (ID) models, for canal control is presented. The former is a simplified hydrological model which is very simple and extensively used in hydraulic engineering for simulation and prediction. The latter is also a model with physical meaning and is widely used for irrigation canals control. Due to a lack of general awareness of Muskingum prediction model in regulation from the control community, authors present this comparative study with the ID control model. Both models have been studied and analyzed for control purposes. This study has been carried out and validated in a real irrigation canal, at Aghili irrigation district in Iran, using two traditional control approaches, PID with feedback and predictive control. The results demonstrate the advantages and drawbacks of both models, showing the benefits and limitations of using the widespread Muskingum model among the hydraulics scientific community for control design.

Sign in / Sign up

Export Citation Format

Share Document