基于参考模型神经网络的锅炉燃烧系统控制

大纯时延、煤种多变、蒸汽负荷频繁变化是链条炉难以进行良好燃烧控制的原因。本文作者提出了改变神经网络输入样本区间，利用网络输出期望值与输出实际值之间的误差平方和产生的突变，辨识出非线性对象的延迟时间的方法，将神经网络大延迟系统的辨识与基于模型预测的神经网络控制策略相结合，可用于对具有变化参数或不确定性延迟时间的非线性大延迟系统的控制，同时，以10t／h链条炉作为研究对象进行仿真，仿真结果表明这种神经网络模型对非线性大纯时延系统的控制具有控制速度快，鲁棒性能好等优点。

Boiler Combustion Control System Based on Model Reference Neural Network

It is difficult to have good performance for a chain boiler combustion control system due to large delay time, varying coal's quality and steam load. A neural network identification method for nonlinear system's delay time is discussed. Using the abrupt mutation resulted from the training error sum square of the real output and the expected output of the network, this method changes the input sample period of the neural network so that it can discriminate the delay time of the nonlinear model. Combining the discrimination of neural network system with long time delay and the control method based on model reference, it can be applied to control the nonlinear long delay time system with variable parameters or unknown delay time. The results obtained from the simulation with a 10t/h chain boiler model show that it has much advantage in better celerity and robustness.