摘要
磨矿分级系统具有诸多不确定因素、非线性、时变、难以建立精确的数学模型,对其溢流浓度进行较好的控制,一直是大家研究的课题。本文提出一种改进型单神经元自适应PID控制方案,因为神经元具有学习特性,该方案能对比例、积分、微分系数进行调整。仿真结果表明,该算法能够适应被控对象较大范围的变化,具有较强的适应能力和鲁棒性。
In milling-classification operation system,there are many uncertainty factors, nonlinear and time-variation. It is difficult to establish mathametical modelling. How to control overflow density effectively is a problem. This paper presents a improved single-neural unit adaptived PID control scheme. It can regulate the coefficients of propotion, integrator and derivative, because neural unit can study. Simulation results show that this method is able to adapt larger range variation of plant and has strong adaptive ability and robustness.
出处
《有色金属（选矿部分）》
CAS
2005年第2期32-34,共3页
Nonferrous Metals（Mineral Processing Section）
关键词
磨矿分级系统
神经元PID控制
溢流浓度
milling-classification operation system
single-neural unit adaptived PID control
overflow density
