无缝钢管张减过程平均壁厚控制迭代自学习方法

ITERATIVE LEARNING CONTROL OF SEAMLESS PIPE AVERAGE WALL THICKNESS IN STRETCH REDUCING PROCESS

迭代学习控制针对具有重复运行性质的系统 ,利用系统实际输出与期望输出的偏差信号 ,产生新的控制信号 ,使得系统跟踪调节性能得以提高。根据张减过程轧制前后钢管壁厚的实测数据和钢管的特征数据 ,采用迭代自学习控制算法 ,提出了无缝钢管张减过程的平均壁厚控制的迭代自学习控制。该控制技术在轧制过程中在线自适应调整各轧制机架的稳态转速分布 ,补偿由物理参数的时变不确定性和建模误差造成的轧辊转速分布参数误差。

The iterative learning control is a high performance control technique for the systems or prcocesses of repetive operation,which produces a new input signal in response to the error signal between the actual outputs and desired outputs Based on the iterative learning control rule,the online learning algorithms for stretch reducing process of seamless pipe were proposed for average wall thickness control (WTCA),in which the measured wall thickness and specifications of the tubes are used to compensate for the disturbance caused by the system uncertainty and the mathematical model error through adjusting rollers speed adaptively The results of simulation and experiments demonstrated the effectiveness of this new method