scholarly journals Nonlinear Fuzzy Model Predictive Control for a PWR Nuclear Power Plant

2014 ◽  
Vol 2014 ◽  
pp. 1-10 ◽  
Author(s):  
Xiangjie Liu ◽  
Mengyue Wang
Keyword(s):  
Power Plant ◽  
Nuclear Power Plant ◽  
Model Predictive Control ◽  
Predictive Control ◽  
Nuclear Power ◽  
High Efficiency ◽  
Fuzzy Model ◽  
Nonlinear Constraint ◽  
Pressurized Water ◽  
Plant Safety

Reliable power and temperature control in pressurized water reactor (PWR) nuclear power plant is necessary to guarantee high efficiency and plant safety. Since the nuclear plants are quite nonlinear, the paper presents nonlinear fuzzy model predictive control (MPC), by incorporating the realistic constraints, to realize the plant optimization. T-S fuzzy modeling on nuclear power plant is utilized to approximate the nonlinear plant, based on which the nonlinear MPC controller is devised via parallel distributed compensation (PDC) scheme in order to solve the nonlinear constraint optimization problem. Improved performance compared to the traditional PID controller for a TMI-type PWR is obtained in the simulation.

scholarly journals An improved fuzzy model predictive control algorithm based on the force/position control structure of the five-degree of freedom redundant actuation parallel robot

2018 ◽  
Vol 15 (5) ◽  
pp. 172988141880497 ◽  
Author(s):  
Shuhuan Wen ◽  
Jianhua Chen ◽  
Guiqian Qin ◽  
Qiguang Zhu ◽  
Hongbin Wang
Keyword(s):  
Model Predictive Control ◽  
Predictive Control ◽  
Control Algorithm ◽  
Position Control ◽  
Sudden Change ◽  
Fuzzy Model ◽  
Good Control ◽  
Parallel Robot ◽  
Traditional Model ◽  
Model Predictive Control Algorithm

In this article, two new algorithms of the redundant force branch of 6-PUS/UPU parallel robot are proposed. They are model predictive control combining with proportional, integral, and differential algorithm and fuzzy combining with model predictive control algorithm. The shortcoming of the traditional model predictive control algorithm is complex adjustment, large amount of calculation, the dynamic performance effect of the system. The proposed PID model predictive control algorithm can make the controller parameters adjustment more convenient. However, PID model predictive control algorithm can’t obtain good control performance under sudden change in situation. Combining model predictive control algorithm with fuzzy theory, fuzzy model predictive control algorithm has better anti-interference ability than PID model predictive control algorithm and can reduce predictive horizon length as possible as it can. Simulation results show that fuzzy model predictive control algorithm can effectively improve real-time performance of control system, the dynamic tracking performance and robustness than the traditional model predictive control and PID model predictive control algorithm.

scholarly journals Reference Tracking using a Non-Cooperative Distributed Model Predictive Control Algorithm

2016 ◽  
Vol 49 (7) ◽  
pp. 1079-1084 ◽  
Author(s):  
Anca Maxim ◽  
Clara M. Ionescu ◽  
Constantin F. Caruntu ◽  
Corneliu Lazar ◽  
Robin De Keyser
Keyword(s):  
Model Predictive Control ◽  
Predictive Control ◽  
Control Algorithm ◽  
Distributed Model ◽  
Reference Tracking ◽  
Distributed Model Predictive Control ◽  
Model Predictive Control Algorithm
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