摘要
提出了基于有限脉冲响应(FIR)模型的预测函数控制(PFC)算法,给出了采用1个基函数(阶跃函数)和2个基函数(阶跃函数、斜坡函数)的控制律的解析解,分析了闭环系统稳态特性,结果表明,系统对于设定值变化和输出扰动均无余差。结合该算法、T-S模糊建模和自适应控制技术,提出了模糊自适应预测函数控制(FAPFC)策略,该方法实现简单,对工况变化具有优良的适应性。针对某超临界600MW直流锅炉主汽温对象,设计预测函数-比例(PFC-P)串级控制系统,经4种典型工况下的阶跃响应试验表明,采用PFC-P串级控制策略的主汽温系统具有良好的动态性能,明显优于采用PID-P串级控制策略的主汽温系统;为了克服负荷变化对主汽温系统性能的影响,采用了FAPFC策略,仿真结果表明,系统具有良好的负荷适应性,负荷大范围升降时,主汽温度变化能维持在5℃以内,而且控制量变化平稳,具有较高的工程实用价值。
A novel Predictive Functional Control (PFC) algorithm based on Finite Impulse Response (FIR) model is proposed. Analytical control law of PFC with one base function (step function) and two base functions (step and ramp function) are provided. Closed-loop system stability is discussed, which shows that the control system both has not reminent difference due to the variety of set point and output disturbance. Combining the novel algorithm, T-S fuzzy modeling and adaptive control technique, Fuzzy Adaptive Predictive Functional Control (FAPFC) scheme is presented. The scheme is easy to be realized in computer or Distributed Control System (DCS), and has excellent adaptability to operating regime variety. Considering the main steam temperature plant of a supercritical once-through 600MW boiler, Predictive Functional Control-Proportional (PFC-P) cascade control system is designed. The step response results under four typical operating regimes show that the main temperature system adopted PFC-P cascade control strategy has favorable dynamic propertyies, and performs far better than the system with PID-P cascade control strategy. In order to get over load variety, FAPFC scheme is applied to main steam temperature system. Simulation results show that the system has good load adaptability. The variety of main steam temperature is under 5℃ and manipolated variable changes smoothly under large-scale variety of load. Therefore, the scheme has hopeful application prospect.
出处
《中国电机工程学报》
EI
CSCD
北大核心
2003年第10期230-235,共6页
Proceedings of the CSEE
关键词
锅炉
主汽温系统
模糊建模
预测函数控制
自适应控制
有限脉冲响应模型
Main steam temperature system
Predictive functional control
Adaptive control
T-S fuzzy modeling
Finite impulse response(FIR) model
