基于混沌理论的过热汽温最优预测控制策略  

Optimal Control of Overheat Steam Temperature Based on Chaos Theory

作　　者：窦春霞[1] 

机构地区：[1]燕山大学电气工程学院,秦皇岛066004

出　　处：《燃烧科学与技术》2003年第4期367-371,共5页Journal of Combustion Science and Technology

基　　金：国家自然科学基金资助项目(60102002);河北省基金资助项目(6011224);霍英东基金资助项目(81057).

摘　　要：由于非线性混沌时间序列内部确定的规律性,其重构相空间具有高精度短期预测性.因此,为实现锅炉过热汽温的非线性、大时滞系统的自适应控制,根据具有混沌特性的过热汽温时间序列重构相空间,计算相空间饱和嵌入维数、最大Lyapunov指数和系统的可预报尺度,并以此为指导,建立神经网络预测模型对过热汽温系统作高精度的短期预测.在此基础上,通过反馈校正,将校正误差和控制增量引入性能函数,寻优得最优控制策略,实现了对过热汽温的非线性、大时滞系统高精度的自适应预测控制.仿真表明了控制的有效性、快速性和鲁棒性.Because of internal certain regularity of chaotic time series, their reconstructing chaotic attractors space has high precision short-term forecast. Therefore, in order to realize adaptive control of overheat steam temperature nonlinear big-lagged system, the chaotic attractors space was reconstructed and systemic embed dimension, maximal Lyapunov exponent and forecast measure were calculated by using the overheat steam temperature time series. A neural-network model was constructed, which can make high precision short-term forecast for the overheat steam temperature system. An optimal controller was designed by using feedback rectification term and control input error being introduced into a performance function and high precision adaptive forecast control was realized for the system. The validity, the high-speed and the robustness are proved by simulated results.

关 键 词：混沌理论 过热汽温 LYAPUNOV指数 神经网络 鲁棒性 

分 类 号：O211.61[理学—概率论与数理统计] TP273[理学—数学]