摘要
针对焦炉集气管压力系统具有强耦合、强干扰、典型非线性、时滞等特点,在系统控制过程中将压力分段考虑,用基于RBF神经网络辨识的单神经元控制器和PID控制相结合的方法,保证集气管压力稳定在工艺要求的范围内;在总管控制级用RBF神经网络预测模型对鼓风机机前吸力的实际输出进行超前预测以克服鼓风机控制系统的时滞。仿真示例和应用结果都表明该方案具有理想的控制效果。
Pressure is measured at different levels in the loop layer because self-adapting predictive decoupling control systems are strongly coupled, disturbed, and non-linear and there is a long time delay for gas collector pressure systems in coke ovens. By combing the traditional neural network control and proportional integral differential（PID） controllers based on radial basis function （RBF） neural network identification, the gas collector pressure is ensured to reach the desired technology range. The prediction model of an RBF neural network is used for advanced prediction of the actual output pressure to overcome delays in general gas collection. The simulation results and application indicate that the method can obtain ideal control results.
出处
《重庆大学学报（自然科学版）》
EI
CAS
CSCD
北大核心
2009年第1期105-110,共6页
Journal of Chongqing University
基金
安徽省教育厅自然科学研究资助项目(KJ2008B104)
关键词
集气管压力
鼓风机系统
预测控制
解耦
神经网络控制
pressure of gas collectors
fan system
predictive control
decoupling
neural network control
