Model-plant mismatch detection and model update for a run-of-mine ore milling circuit under model predictive control

2013 ◽  
Vol 23 (2) ◽  
pp. 100-107 ◽  
Author(s):  
Laurentz E. Olivier ◽  
Ian K. Craig
Keyword(s):  
Model Predictive Control ◽  
Predictive Control ◽  
Model Plant ◽  
Mismatch Detection ◽  
Model Update

An Adaptive Economic Model Predictive Control Approach for Wind Turbines

2017 ◽  
Vol 140 (5) ◽  
Author(s):  
Mohamed L. Shaltout ◽  
Zheren Ma ◽  
Dongmei Chen
Keyword(s):  
Optimal Control ◽  
Model Predictive Control ◽  
Wind Energy ◽  
Wind Turbine ◽  
Predictive Control ◽  
Economic Model ◽  
Adaptive Approach ◽  
Energy Capture ◽  
Model Plant ◽  
Economic Model Predictive Control

Motivated by the reduction of overall wind power cost, considerable research effort has been focused on enhancing both efficiency and reliability of wind turbines. Maximizing wind energy capture while mitigating fatigue loads has been one of the main goals for control design. Recent developments in remote wind speed measurement systems (e.g., light detection and ranging (LIDAR)) have paved the way for implementing advanced control algorithms in the wind energy industry. In this paper, an LIDAR-assisted economic model predictive control (MPC) framework with a real-time adaptive approach is presented to achieve the aforementioned goal. First, the formulation of a convex optimal control problem is introduced, with linear dynamics and convex constraints that can be solved globally. Then, an adaptive approach is proposed to reject the effects of model-plant mismatches. The performance of the developed control algorithm is compared to that of a standard wind turbine controller, which is widely used as a benchmark for evaluating new control designs. Simulation results show that the developed controller can reduce the tower fatigue load with minimal impact on energy capture. For model-plant mismatches, the adaptive controller can drive the wind turbine to its optimal operating conditions while satisfying the optimal control objectives.

Model Predictive Control of a Direct Fire Projectile Equipped With Canards

2008 ◽  
Vol 130 (6) ◽  
Author(s):  
Douglas Ollerenshaw ◽  
Mark Costello
Keyword(s):  
Model Predictive Control ◽  
Predictive Control ◽  
Short Range ◽  
Control Law ◽  
Impact Point ◽  
Step Size ◽  
Prediction Horizon ◽  
Control Scheme ◽  
Model Update ◽  
Length Gain

Launch uncertainties in uncontrolled direct fire projectiles can lead to significant impact point dispersion, even at relatively short range. A model predictive control scheme for direct fire projectiles is investigated to reduce impact point dispersion. The control law depends on projectile linear theory to create an approximate linear model of the projectile and quickly predict states into the future. Control inputs are based on minimization of the error between predicted projectile states and a desired trajectory leading to the target. Through simulation, the control law is shown to work well in reducing projectile impact point dispersion. Parametric trade studies on an example projectile configuration are reported that detail the effect of prediction horizon length, gain settings, model update interval, and model step size.

Methodology for Detecting Model–Plant Mismatches Affecting Model Predictive Control Performance

2015 ◽  
Vol 54 (48) ◽  
pp. 12072-12085 ◽  
Author(s):  
Viviane Botelho ◽  
Jorge Otávio Trierweiler ◽  
Marcelo Farenzena ◽  
Ricardo Duraiski
Keyword(s):  
Model Predictive Control ◽  
Predictive Control ◽  
Control Performance ◽  
Model Plant
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