过热蒸汽温度量子粒子群优化无模型自适应控制

Model-free adaptive control of superheated steam temperature based on quantum-behaved particle swarm optimization

火力发电厂锅炉过热蒸汽温度不仅具有大滞后特性,且其模型还会随锅炉负荷的变化而变化,故依赖数学模型设计的PID控制一直未取得较好的控制品质。对此,本文提出一种基于量子粒子群的改进无模型自适应控制(QPSO-IMFAC)算法,摆脱了对模型的依赖,利用量子粒子群(QPSO)算法对IMFAC算法控制参数寻优,解决了传统无模型自适应控制(MFAC)算法控制参数难以确定的问题。仿真结果表明:采用QPSO-IMFAC算法控制锅炉过热蒸汽温度时,系统具有强扰动抑制性和高鲁棒性,QPSO-IMFAC算法对锅炉过热蒸汽温度控制是有效的;对阶跃响应和定值扰动的控制效果均优于QPSO-PID算法。因此,可以将IMFAC算法实现于DCS控制中,且使用QPSO-IMFAC算法优化出的控制参数可作为锅炉过热蒸汽温度调节的基准值。

The temperature of superheated steam of boiler in thermal power plants has large delay property,and its model changes with the boiler load,which causes the PID controller designed on the basis of mathematical model can not realize good control effects.Against this problem,this paper proposes an improved model free adaptive control algorithm based on quantum-behaved particle swarm optimization（QPSO-IMFAC）.This algorithm gets rid of the dependence on the model.Using the quantum-behaved particle swarm optimization（QPSO）algorithm to find the optimal control parameters of the improved model free adaptive control algorithm（IMFAC）can overcomes the shortcomings that the parameters of the conventional MFAC algorithm are difficult to be ascertained.The simulation test result shows that,the boiler system has strong anti-disturbance ability and high robustness when using the QPSO-IMFAC algorithm to control the superheated steam temperature,indicating the QPSO-IMFAC algorithm is superior to the conventional QPSO-PID algorithm in step response and set value disturbance control.