针对火电厂主蒸汽温度系统大惯性、大迟延、非线性的特点,常规串级PID控制难以取得满意的调节效果,为了改善常规PID控制的不足,文章在研究BP神经网络的基础上,把BP神经网络PID控制应用到主汽温控制系统中。运用matlab仿真,结果表明,与传统控制相比BP神经网络PID控制算法有效减小了系统的超调量,提高了系统的响应速度,在主汽温控制系统中具有很好的控制效果。 In view of the large inertia, large delay and non-linearity of main steam temperature system in thermal power plants, conventional cascade PID control can not achieve satisfactory adjustment effect. In order to improve the deficiencies of conventional PID control, based on the study of BP neural network, Neural network PID control applied to the main steam temperature control system. The results of Matlab simulation show that the BP neural network PID control algorithm can effectively reduce the overshoot of the system and improve the response speed of the system compared with the traditional control, which has a good control effect in the main steam temperature control system.