流化床燃烧系统模糊-神经元PID解耦补偿控制

依据神经元控制和解耦补偿的思想，引入了一种自适应神经元解耦补偿器，给出了神经元权系数的在线学习方法。在此基础上，通过将模糊控制技术和神经元自适应PID控制技术相结合，提出了一种不依赖于被控对象精确数学模型的多变量解耦控制方案。将该种方法应用于流化床燃烧系统控制，对耦合强烈的流化床锅炉床温、主汽压力、烟气含氧量三维传递函数矩阵进行解耦控制。仿真研究表明，该方案解耦效果良好，并且可以有效克服流化床燃烧对象的大滞后和非线性，获得良好的控制品质。

Fuzzy-neural PID Decoupling Compensation Control in Circulating Fluidized Bed Boiler Combustion System

An adaptive neural decoupling compensation method and an on-line learning mechanism for the neuron weights were presented based on the principle of neural control and decoupling control. By combining fuzzy control and neuron adaptive PID control, a decoupling control method that is independent of the accurate mathematical models of the controlled plant was proposed. The method was applied to the decoupling control of the combustion system of a circulating fluidized bed boiler (CFBB) where the three controlled variables (bed temperature, main steam pressure, and flue gas oxygen content) are strongly coupled. Simulation results show that the method can achieve good decoupling effects, and can overcome the large time delay and the nonlinearity that exist in the CFBB.