Combine Evolutionary Optimization with Model Predictive Control in Real-time Flood Control of a River System

2015 ◽  
Vol 29 (8) ◽  
pp. 2527-2542 ◽  
Author(s):  
Po-Kuan Chiang ◽  
Patrick Willems
Keyword(s):  
Model Predictive Control ◽  
Real Time ◽  
Predictive Control ◽  
Flood Control ◽  
River System ◽  
Evolutionary Optimization

scholarly journals A Model Predictive Control Framework for Real-Time Optimisation of Water System Operations

10.29007/fg7g ◽  
2018 ◽  
Author(s):  
Henrik Madsen ◽  
Anne Katrine Falk ◽  
Rasmus Halvgaard
Keyword(s):  
Model Predictive Control ◽  
Real Time ◽  
Case Studies ◽  
Predictive Control ◽  
Large Scale ◽  
Water System ◽  
River System ◽  
Quadratic Program ◽  
Real Time Control ◽  
Time Control

We have developed a versatile Model Predictive Control (MPC) framework, which can handle real-time control of a large variety of water systems. The framework combines a fast-solvable optimisation model (a quadratic program) with evaluation and realignment by a detailed hydrological-hydrodynamic model. The flexibility of the MPC framework is highlighted by two case studies: (1) a large-scale river system with several weeks of travel time, and (2) an urban storm and wastewater system with a concentration time of about half an hour to one hour. Both case studies demonstrate a large potential for improving operations by system-wide real-time optimisation.

scholarly journals Real-Time Flood Control by Tree-Based Model Predictive Control Including Forecast Uncertainty: A Case Study Reservoir in Turkey

Water ◽  
2018 ◽  
Vol 10 (3) ◽  
pp. 340 ◽  
Author(s):  
Gökçen Uysal ◽  
Rodolfo Alvarado-Montero ◽  
Dirk Schwanenberg ◽  
Aynur Şensoy
Keyword(s):  
Model Predictive Control ◽  
Real Time ◽  
Predictive Control ◽  
Flood Control ◽  
Forecast Uncertainty

Real-time thermal management for two-level active rectifier with finite control set model predictive control

2021 ◽  
Vol 131 ◽  
pp. 107057
Author(s):  
Gokhan Ozkan ◽  
Phuong H. Hoang ◽  
Payam Ramezani Badr ◽  
Chris S. Edrington ◽  
Behnaz Papari
Keyword(s):  
Model Predictive Control ◽  
Real Time ◽  
Thermal Management ◽  
Predictive Control ◽  
Active Rectifier ◽  
Finite Control ◽  
Control Set

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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