基于迭代学习的矿井提升系统跟踪控制

Mine hoist system tracking control based on iterative learning

矿井提升机在煤矿开采和生产过程中发挥着至关重要的作用。针对矿料提升过程具有较强重复性的特点,提出迭代学习的提升机速度和位置跟踪控制方法。设计了提升机的D型迭代学习控制器,同时考虑运行过程中出现的非重复性干扰设计了带有滤波器型迭代学习控制器。并采用λ范数证明了系统的收敛性,理论结果表明矿井提升机位置与速度跟踪误差可以收敛到0。同时仿真结果表明,经过30次运行后,跟踪误差几乎收敛到0,迭代学习控制算法可利用矿井作业的重复运行特性可以有效提高提升机的跟踪性能,带有滤波器的迭代学习控制算法可较好地抑制了非重复扰动的影响。

The mine hoist plays a vital role in the process has strong repeatability, the method of hoist speed and position tracking control based on iterative learning is proposed. D-type iterative learning controller of lifting machine is designed, and considering the non-repeat dis turbances in the operation process the filter type iterative learning controller is designed. The rigorous analysis of convergence in the iteration domain is based on A norm. The theoretical results show that iterative learning control algorithm guarantees the convergence position, and the velocity tracking error of mine hoist system can converge to zero. The simulation results show that, after 30 times of operation, the tracking error almost converges to 0, the it erative learning control algorithm can effectively improve the tracking performance of hoist by using repetitive opera tion characteristics of mine operation, and the filter-type iterative learning control algorithm attenuates the effect of non-repetitive disturbance effectively.