基于失配补偿Smith-RBF神经网络的主蒸汽压力控制技术
Main steam pressure control technique based on mismatch compensation Smith predictor and RBF neural network

针对燃气发电锅炉主蒸汽压力控制系统对象的大滞后、不确定性和煤气扰动大的特点，设计了一种基于失配补偿Smith预估及RBF神经网络的控制方案。利用RBF神经网络的在线学习能力整定常规PID的参数，并通过失配补偿Smith预估控制器对系统中存在的纯滞后进行补偿，有效解决了火力发电锅炉主蒸汽压力对象动态特性模型失配及纯滞后的问题。通过仿真研究及实际应用表明：该控制方法对于火力发电锅炉主蒸汽压力控制具有很好的稳定性和抗干扰能力。
To solve the problems of large lag, uncertainty and gas disturbance of main steam pressure control system object of gas-fired power boiler,a control scheme based on mismatch compensation Smith prediction and RBF neural network is designed. The RBF neural network's online learning ability is used to adjust the parameters of the conventional PID, and the mismatch compensation Smith prediction controller to compensate the pure hysteresis in the system.The improved algorithm effectively solves the problem of mismatch of dynamic characteristic model and pure lag of main steam pressure object in thermal power boiler. The simulation research and practical application show that the control method has good stability and anti-interference ability for the main steam pressure control of thermal power boilers