基于模型参考的锅炉燃烧系统神经网络控制和辨识

大纯时延、煤种多变和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。对非线性延迟系统延迟时间的神经网络辨识方法进行了研究,即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,可以辨识出非线性对象的延迟时间。将神经网络大延迟系统的辨识与基于神经网络动态补偿的模型参考自适应控制策略相结合,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。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。

Neural network control and identification for boiler combustion system based on models

It is difficult to have good performance for 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 reference model,it can be applied to control the nonlinear long delay time system with variable parameters or unknown delay time.Simulating with a 10t/h chain boiler model,the results show it has much better advantage of celerity and robustness.