摘要
板形是板带轧制的重要质量指标,倾辊和弯辊是板形控制的重要手段。目前常规的PID控制算法被广泛应用到板带轧机倾辊和弯辊板形控制系统中,但由于实际系统随机干扰严重,具有多变量、非线性、强耦合的特征,难以建立较为准确的数学模型,常规的PID控制算法很难满足板形高精度控制的要求。为能提高倾辊和弯辊板形控制系统的性能,在常规PID控制算法的基础上,建立基于神经网络的模糊PID倾辊弯辊板形控制模型,通过神经网络的自学习能力和模糊控制的'概念'抽象能力的有机结合寻找一个最佳的P、I、D非线性组合控制律,增强对控制环境变化的适应能力和自学习能力。仿真试验结果表明,该模型能很好地跟踪板形的目标设定值,响应快,超调小,鲁棒性强,可提高倾辊和弯辊对板形的控制精度,为板形高精度控制提供了一种新方法。
Flatness is an important quality indicator in strip rolling, and tilting roll and bending roll are important means of flatness control. Now conventional PID control algorithm is widely applied to tilting roll and bending roll flatness control system for strip mill, but it is very difficult to build an exact math model and conventional PID control algorithm is hard to meet the need of high-precision flatness control because of actual system＇s severe random disturbance and multivariable, non-linear and strong coupling characters. Fuzzy PID tilting roll and bending roll flatness control model based on neural network is built on the ground of conventional PID control algorithm. An optimized non-linear combined control rule of P, I, and D is built by the self-learning ability of neural network and the ＂conception＂ abstraction ability of fuzzy control to strengthen the ability of self-adaptation and self-leaning to the change of control environment. Simulation results of the model indicate that flatness target value is tracked well with rapid response, small overshot and strong robust. In conclusion, flatness control precision of tilting roll and bending toll is inereased and a new method is provided for high-precision flatness control.
出处
《机械工程学报》
EI
CAS
CSCD
北大核心
2009年第9期255-260,共6页
Journal of Mechanical Engineering
基金
国家自然科学基金(50675186)
河北省自然科学基金(E2006001038)资助项目
关键词
板形控制
倾辊
弯辊
神经模糊
PID
Flatness control Tilting roll Bending roll Neural fuzzy PID
