针对超超临界直流锅炉主汽温控制系统，提出一种基于模糊神经网络模型的分布式监督预测控制(DSPC)策略.在各子系统原有的调节层结构的基础上，增加监督层，将中间点温度和各级过热器出口温度设定值的调整问题转化为在线优化问题.子系统监督层目标函数考虑相关联子系统的性能指标，通过相邻子系统监督层的信息交换和迭代优化，实现各级子系统的协同配合.针对负荷扰动情况进行仿真实验，结果验证了所提出方法的有效性.
Considering the main steam temperature control system in ultrasupercritical oncethrough boiler, this paper presents a distributed supervisory predictive control (DSPC) method based on neurofuzzy network to realize the coordination of each control system. The supervisory layer is added on the basis of the original adjusting layer, so the setting values of the intermediate point temperature and superheated steam temperature at all levels can be obtained by solving an online optimization problem. Cooperation between systems is achieved by exchanging information between each control system and its neighbors via supervisory layer and by optimizing the local problem with the performance index considering the related systems. Simulation experiment is carried out under the condition of disturbance load to show the effectiveness of the proposed controllers.
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