基于自适应混沌粒子群算法的冷却剂平均温度预测控制

PREDICTIVE CONTROL OF COOLANT AVERAGE TEMPERATURE BASED ON ADAPTIVE CHAOTIC PARTICLE SWARM OPTIMIZATION

为提高核反应堆预测控制求解最优控制律的效率和准确性,将堆芯预测模型拓展为冷却剂平均温度预测模型,基于自适应混沌粒子群算法,结合反应性增量约束条件和在线参数辨识对预测控制系统进行滚动优化,最终实现冷却剂平均温度的预测控制。通过MATLAB平台进行模型仿真,实验结果表明,滚动优化过程中改进的混沌粒子群算法能够快速、准确地寻优;在负荷升、降过程中,该控制方法也保证了冷却剂平均温度快速、精准地跟踪设定值变化。

In order to improve the efficiency and accuracy of nuclear reactor predictive control to solve the optimal control law,the core prediction model is extended to the coolant average temperature prediction model.Based on the adaptive chaotic particle swarm optimization(ACPSO),combined with the reactivity increment constraint conditions and online parameter identification,the predictive control system performed rolling optimization,which realized the predictive control of the average coolant temperature.Simulation on the MATLAB platform was carried out.The results show that the improved ACPSO can quickly and accurately find the optimization during the rolling optimization process.During the load rise and fall,the control method also ensures that the average coolant temperature can track the set value quickly and accurately.