Authors:
Man Gyun Na
1
and
Belle R. Upadhyaya
2
Affiliations:
1
Chosun University, Korea, Republic of
;
2
The University of Tennessee, United States
Keyword(s):
Genetic algorithm, Model predictive control, Reactor power control, SP-100 space reactor, and Support vector machines.
Related
Ontology
Subjects/Areas/Topics:
Informatics in Control, Automation and Robotics
;
Intelligent Control Systems and Optimization
;
Neural Networks Based Control Systems
Abstract:
In this work, a model predictive control (MPC) method combined with support vector regression (SVR), is applied to the design of the thermoelectric (TE) power control in the SP-100 space reactor. The future TE power is predicted by using SVR. The objectives of the proposed model predictive controller are to minimize both the difference between the predicted TE power and the desired power, and the variation of control drum angle that adjusts the control reactivity. Also, the objectives are subject to maximum and minimum control drum angle and maximum drum angle variation speed. The genetic algorithm (GA) is used to optimize the model predictive controller. A lumped parameter simulation model of the SP-100 nuclear space reactor is used to verify the proposed controller. The results of numerical simulations to check the performance of the proposed controller show that the TE generator power level controlled by the proposed controller could track the target power level effectively, satis
fying all control constraints.
(More)