采用神经网络控制,选取应用最广泛的BP算法,与传统PID控制结合的控制策略来实现对主汽温的有效控制,仿真结果表明基于BP神经网络的自整定PID控制具有良好的自适应能力和自学习能力,对大迟延和变对象的系统可取得良好的控制效果,是采用多策略的智能控制与PID结合实现主汽温控制的一次有益的尝试与探索。 The neural network control is used to select the most widely used BP algorithm and the control strategy combined with the traditional PID control to achieve effective control of the main steam temperature. The simulation results show that the self-tuning PID control based on BP neural network has good self-adaptive ability and Self-learning ability, which can achieve good control effect on the system of large delay and variable objects. It is a useful attempt and exploration to realize the main steam temperature control by combining the multi-strategy intelligent control and PID.