Model Predictive Control of an SP-100 Space Reactor Using Support Vector Regression and Genetic Optimization

In this work, a model predictive control method combined with support vector regression and genetic optimization 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 the support vector regression. 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 constrained by maximum and minimum control drum angle and maximum drum angle variation speed. The genetic algorithm that is effective in accomplishing multiple objectives 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, satisfying all control constraints