面向窑压控制的基于改进STS模型的非线性广义预测控制研究

摘要

Furnace pressure is one of the important variable to be controlled in the process of glass furnace operation.Owing to the influence of a large number of uncertainties,existent control algorithms tend to produce overshooting and frequent fluctuation in follow-up.Considering that simplified T-S model (STS) has a strong self-adaptive learning capability and subtractive clustering is able to handle the unsteadiness in the optimal model structure,this paper proposes,for the purpose of furnace pressure control,a generalized fuzzy nonlinear prediction approach by combining subtractive clustering and STS.This approach allows a quick approximation of the object following jumps of furnace pressure and may eliminate system unsteadiness caused by jumps.Additionally,in respect of excessive overshooting in furnace pressure step following-up,a control method is designed based on the improved STS model and integrating global optimal rolling optimization function with optimal control increment algorithm so that overshooting is restrained essentially.This approach involves little computation work and is easy to implement.%窑压是玻璃窑炉运行过程中重要的被控指标之一,由于受到大量不确定因素影响,现有控制算法的跟踪超调大且波动频繁.鉴于简化T-S模型(STS)的强在线自适应学习能力和减法聚类可以解决最优模型结构的不稳定问题,本文研究一种基于减法聚类与简化T-S模型相融合的非线性模糊广义预测方法实现窑压的控制.利用该方法可以快速的完成窑压跳变后对象的逼近,克服由于跳变所引起的系统不稳定现象;在改进的STS模型的基础上,针对窑压阶跃跟踪超调过大的问题,结合整体最优滚动优化函数及最优控制增量快速求解方法设计控制律,从本源出发抑制超调,计算量小,并易于实施.