Research on pressurizer water level control of nuclear reactor based on RBF neural network and PID controller
Research on pressurizer water level control of nuclear reactor based on RBF neural network and PID controller
复制标题
DOI：
10.1109/icmlc.2010.5580842
复制
发表时间：
2010-07
期刊：
影响因子：
--
通讯作者：
Jian-Hua Ye;Jin-Ming Yi;Hua-Yan Ji
中科院分区：
文献类型：
--
作者：
Jian-Hua Ye;Jin-Ming Yi;Hua-Yan Ji
在现代反应堆中，稳压器的水位采用常规的PID控制器。稳压器是一种大滞后、非线性、多扰动的被控对象。要建立精确的数学模型是很困难的。因此不能满足控制要求。受神经网络反馈调节机制的启发，提出了一种基于径向基函数自整定的复合控制策略。RBF神经网络具有较强的鲁棒性和较强的自适应能力，能够适应被控对象参数的变化，易于在线实现。仿真研究表明，该控制器具有较强的鲁棒性和自适应能力，控制精度较高
In modern reactors a conventional PID controller is adopted in water level of pressurizer. Pressurizer is a controlled object which has the characteristics of large-delay, nonlinear and multi-disturbance. It is difficult to have precise mathematical model. So it cannot be satisfied with control requirements. Prompted by the feedback regulation mechanism of neural network, a composite control strategy based on RBF tuning PID control is presented in this paper. RBF neural network has the characteristics of strong robustness and better self-adaptive ability, which can be adaptive to the change in the parameters of the controlled plant and be easily accomplished on-line. A simulation study shows that it has strong robustness and adaptive abilities and higher control accuracy