Real-Time Production Optimization of Steam-Assisted-Gravity-Drainage Reservoirs Using Adaptive and Gain-Scheduled Model-Predictive Control: An Application to a Field Model

2019 ◽  
Vol 34 (01) ◽  
pp. 072-089 ◽  
Author(s):  
Rajan G. Patel ◽  
Vinay Prasad ◽  
Japan J. Trivedi
Keyword(s):  
Model Predictive Control ◽  
Real Time ◽  
Predictive Control ◽  
Field Model ◽  
Production Optimization ◽  
Gravity Drainage ◽  
Time Production ◽  
Steam Assisted Gravity Drainage ◽  
Gain Scheduled

Real-time multivariable model predictive control for steam-assisted gravity drainage

AIChE Journal ◽  
2018 ◽  
Vol 64 (8) ◽  
pp. 3034-3041 ◽  
Author(s):  
Sagar N. Purkayastha ◽  
Ian D. Gates ◽  
Milana Trifkovic
Keyword(s):  
Model Predictive Control ◽  
Real Time ◽  
Predictive Control ◽  
Gravity Drainage ◽  
Multivariable Model ◽  
Steam Assisted Gravity Drainage

scholarly journals An Adaptive RBF-NMPC Architecture for Trajectory Tracking Control of Underwater Vehicles

Machines ◽  
2021 ◽  
Vol 9 (5) ◽  
pp. 105
Author(s):  
Zhenzhong Chu ◽  
Da Wang ◽  
Fei Meng
Keyword(s):  
Model Predictive Control ◽  
Prediction Model ◽  
Real Time ◽  
Predictive Control ◽  
Trajectory Tracking ◽  
Tracking Control ◽  
Working Environment ◽  
Gray Wolf ◽  
Trajectory Tracking Control ◽  
System Output

An adaptive control algorithm based on the RBF neural network (RBFNN) and nonlinear model predictive control (NMPC) is discussed for underwater vehicle trajectory tracking control. Firstly, in the off-line phase, the improved adaptive Levenberg–Marquardt-error surface compensation (IALM-ESC) algorithm is used to establish the RBFNN prediction model. In the real-time control phase, using the characteristic that the system output will change with the external environment interference, the network parameters are adjusted by using the error between the system output and the network prediction output to adapt to the complex and uncertain working environment. This provides an accurate and real-time prediction model for model predictive control (MPC). For optimization, an improved adaptive gray wolf optimization (AGWO) algorithm is proposed to obtain the trajectory tracking control law. Finally, the tracking control performance of the proposed algorithm is verified by simulation. The simulation results show that the proposed RBF-NMPC can not only achieve the same level of real-time performance as the linear model predictive control (LMPC) but also has a superior anti-interference ability. Compared with LMPC, the tracking performance of RBF-NMPC is improved by at least 43% and 25% in the case of no interference and interference, respectively.

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