【摘要】 为了优化传统核电站稳压器控制，本文将深度学习方法引入PID控制器。将长短期记忆（Long Short-Term Memory,LSTM）模型使用传统PID控制仿真数据训练后，为PID控制器提供工况预测数据，弥补因为传感器信号传输以及PID控制器计算带来的反馈延迟，从而使得PID控制器能够依据更实时的工况进行控制信号的计算。在验证实验中，对基于上述方法的智能PID控制器进行了功能验证和复杂工况运行验证。实验结果表明，智能PID控制器能够有效降低传统PID控制过程中的超调量（最高可降低80.7%），同时可以将传统PID控制达到稳态的时间缩短最多60.73s。该控制器的控制性能虽然受工况变化影响仍然较大，但是为核电站稳压器的智能优化方法进行了有益探索，为后续进一步利用人工智能方法改进传统核电站仪控方法提供了借鉴。更多还原

【Abstract】 With a consideration of alleviating the unstable control responses in traditional pressurizer control, this work adopts the cutting-edge deep learning method to optimize the PID control performance. A Long Short-Term Memory(LSTM) model is trained by data from a traditional PID control simulation and is then used to provide predictions to the PID controller such that the newly constructed intelligent controller can produce control signals for real time working conditions. The verification experiments conducted for both functionality and complex inputs successfully proved the advantages of the intelligent controller, showing up to 80.7% overshoot reduction and up to 60.73 seconds decrease in the control time for a steady state. Although its performance varies in different control cases, it does provide a deep learning-based control option for the pressurizer control in nuclear power plants.更多还原