【摘要】 对具有滞后的线性大工业过程提出了加权超前开环PD 型的迭代学习控制算法,建立了基本的迭代学习控制结构,给出了迭代学习控制算法关于控制系统的ε 收敛性的定义和证明,提出了理想轨线的δ 可达性的概念.数字仿真表明,迭代学习控制能有效改善控制系统的动态品质,如缩短过渡时间,抑制超调等,表明了迭代学习控制算法的有效性.更多还原

【Abstract】 In this paper, the weighted leading openloop PDtype iterative learning control algorithm for linear largescale industrial processes with time delays is studied. The basic iterative learning control structure is established. The εconvergence of the algorithms with respect to the control systems is defined and proved. The δreachability of the desired trajectories is given. Digital simulations show that the iterative learning control algorithms can remarkably improve the transient response performance of the systems, such as shorten the settling time and decrease the overshoot while the setpoints changes are imposed on real industrial processes control systems etc. This indicates the effectiveness of the iterative learning control algorithms.更多还原