连续浇注过程钢水液位精细迭代控制

Refined Iterative Control on Molten Steel Level in A Continuous Casting Process

为保证具有强非线性、建模困难和具有周期性扰动连续浇注过程中钢水液位满足工艺和生产的要求,从而实现连续浇注过程中钢水液位的高品质控制,提出了一种精细迭代控制策略来进一步削弱连续浇注过程中钢水液位的膨胀扰动带来的不利影响。该策略是一种包含P型学习律的迭代学习算法,这种P型学习律除引进遗忘因子和开关装置外,还特别利用了前两次迭代过程中的误差信息(即控制律为PID+遗忘因子+开关函数+误差精细信息的有机组合),以使误差信息精细化,从而进一步改善迭代学习控制的效果;研究表明在系统同时具有模型不确定性,周期性膨胀扰动,可测噪声干扰与初始状态误差情况下能保证系统的输入信号误差、状态误差和输出误差的最终有界性,实现了钢水液位的高品质控制;计算机仿真进一步验证了所提方案的正确性和可行性。

In order to guarantee molten steel level in a continuous casting process satisfying the standard requirement of the technique, a refined iterative learning control strategy was put forward, which is one kind of type P iterative learning control, and besides the forget- ting factor and the on-off switching action, error information was further refined on account of introduction of the just past and the second past cycles error signals. Results demonstrated that, the control quality can still he improved even under the model uncertainties, periodic bulging disturbances, and the measuring noises, as the input signal error, the state error and the output error were ultimately bounded. Simulation results were provided to clarify the suggested idea further.