Nuclear power predictive sliding mode control based on state observer

Da Tan; Gang Zhou

doi:10.1117/12.2680912

23 May 2023 Nuclear power predictive sliding mode control based on state observer

Da Tan, Gang Zhou

Proceedings Volume 12645, International Conference on Computer, Artificial Intelligence, and Control Engineering (CAICE 2023); 126455L (2023) https://doi.org/10.1117/12.2680912
Event: International Conference on Computer, Artificial Intelligence, and Control Engineering (CAICE 2023), 2023, Hangzhou, China

Abstract

Nuclear reactor is the energy core of nuclear power plant. In order to ensure the efficient and stable operation of nuclear power plant and continuously output the thermal power generated by nuclear fission, it is necessary to control the power of reactor under operating conditions. The nonlinear mathematical model of nuclear reactor core power is established by perturbation method for point-reactor kinetics equation, and a reduced-order Luenberger state observer is designed to observe and track the state variables that cannot be measured directly in the model. On this basis, a sliding mode control algorithm based on model prediction is designed to realize the state feedback control of reactor power. Compared with the traditional PID control algorithm and verified by simulation with MATLAB/Simulink software. The simulation results show that in the up and down stage of load power, the speed fluctuation of control rod of sliding mode controller (0.5-mm/s) is higher than that of PID controller (0.07-mm/s), but in the steady-state conditions, the relative power tracking error of sliding mode controller (4×10^-4) is less than that of PID controller (3×10^-3). The predictive sliding mode controller designed in this paper has better comprehensive performance than PID controller.

Citation Download Citation

Da Tan and Gang Zhou "Nuclear power predictive sliding mode control based on state observer", Proc. SPIE 12645, International Conference on Computer, Artificial Intelligence, and Control Engineering (CAICE 2023), 126455L (23 May 2023); https://doi.org/10.1117/12.2680912

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