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对递阶稳态优化下非线性大工业过程施行迭代学习控制 ,目的是进一步改善大工业过程的动态品质 .建立迭代学习控制的基本结构 ,提出迭代学习控制算法关于控制系统的ε-收敛性和期望目标轨线的δ -可达性的概念 ,对具有死区与滞后的饱和非线性大工业过程控制系统给出加权超前开环PD-型迭代学习算法 .利用 Bellman-Gronwall不等式和λ范数理论 ,论证了算法的收敛性 .数字仿真表明 ,迭代学习控制能有效改善递阶稳态下非线性大工业控制系统的动态品质 .
The purpose of iterative learning control is to further improve the dynamic quality of large-scale industrial processes. The basic structure of iterative learning control is established. The iterative learning control algorithm is proposed to control the ε-convergence of the control system and Expecting the concept of δ - reachability of the target trajectory, a weighted leading open - loop PD - type iterative learning algorithm is given to a large - scale nonlinear saturated industrial process control system with dead zone and hysteresis. By using Bellman - Gronwall inequality and λ norm Theory, the convergence of the algorithm is demonstrated.The numerical simulation shows that iterative learning control can effectively improve the dynamic quality of large-scale non-linear industrial control system with steady state.