Optimized Thermal Power Control for Nuclear Superheated-Steam Supply Systems Based on Multi-Layer Perception

Abstract Nuclear superheated-steam supply systems (Su-NSSS) produces superheated steam flow for electricity generation or process heat. Though the current Su-NSSS control law can guarantee satisfactory closed-loop stability, which regulates the nuclear power, primary coolant temperature and live steam temperature through adjusting the control rod speed as well as primary and secondary flowrates, however, the control performance needs to be further optimized. Motivated by the necessity of optimizing the thermal power response, a novel multi-layer perception (MLP) based model predictive control (MPC) is proposed in this paper. The thermal power of Su-NSSS is predicted by an MLP with online learning algo-rithm, and the control input is designed in the direction opposite to the gradient of a given performance index. Then, it is proved that this MLP-based MPC guarantees globally-bounded closed-loop stability. Finally, this newly-built MLP-based MPC for thermal power is implemented by forming a cascaded feedback control loop with the current Su-NSSS controller in the inner loop and this MPC in the outer loop. Numerical simulation results verify the correctness of theoretical result, and show the satisfactory improvement in optimizing the thermal power response.

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