Predictive Functional Control for Tracking of Core Power Variations in Pressurized Water Reactor based on Laguerre functions and Reduced-Order Model

In this paper, the design of predictive functional controller based on Laguerre functions to track the load changes in Pressurized Water Reactor (PWR) nuclear power stations has been considered. Since, despite of out-performance of predictive controllers in industrial applications, their implementation implies high computational complexity for constrained large scale systems, in this paper, the design of model predictive controller with low computational complexity was considered. For this purpose, at first, the order of PWR model was reduced via Balanced Truncation method. Then, due to low computational complexity and high performance of predictive functional controllers, we dealt with the design of predictive functional controllers based on Laguerre functions. In this context, the Laguerre polynomial scaling parameter was determined by minimizing integral square error. Then, due to mechanical constraints, some specific constraints were applied to the control effort and its changes, and the Quadratic Programming method was used for solving the constrained model predictive control problem and consequently, designing the control effort signal. Also, in order to show the efficiency of the proposed core power control method, the system response in the presence of disturbance is investigated. It is shown that, by using predictive functional controller on a reduced order model, in addition to the decrease of the computation volume, the performance of the core power control to track load changes in presence of external disturbance is well done.