改进MFAILC算法在轧机液压系统的应用

Application of Improved Model-Free Adaptive Iterative Learning Control in Rolling Mill Hydraulic System

针对轧机液压位置闭环系统存在强耦合、多变量等非线性因素,精确建模困难且不具备自我更新学习等问题,将无模型自适应迭代学习控制(MFAILC)应用于轧机液压位置闭环系统。由于MFAILC算法的误差收敛消耗时间较长,采用高阶伪偏导数估计算法改善系统的收敛速度,同时针对MFAILC算法在控制过程中的抗干扰性较差、容易产生控制偏差的问题,结合内模控制强鲁棒性、结构简单等优点,将其引入MFAILC算法,对算法的控制结构进行改进。仿真实验结果表明:改进后的无模型自适应迭代学习算法的收敛速度、控制精度都得到提高,系统的抗干扰性也能够增强。

In view of the strong coupling,multivariable and other nonlinear factors of the rolling mill hydraulic position closed-loop system,the accurate modeling is difficult and there is no self-renewal learning ability,the model-free adaptive iterative learning control(MFAILC)was applied to the rolling mill hydraulic position closed-loop system.Since the error convergence time of the MFAILC algorithm is long,the high-order pseudo-partial derivative estimation algorithm was used to improve the convergence speed of the system.At the same time,aiming at the problems of poor anti-interference and easy to cause control deviation in the control process of MFAILC algorithm,combining with the advantages of strong robustness and simple structure of internal model control,it was introduced into MFAILC to improve the control structure of the algorithm.The simulation results show that the improved algorithm has faster convergence speed and higher control accuracy,the anti-interference performance of the system is also enhanced.