基于逆Lipschitz条件的二阶导数迭代学习控制

Iterative Learning Control with Second Derivative Based on Global Inverse Lipschitz Condition

中文关键词:  收敛速度  学习律  相对度  迭代学习控制  二阶导数  多项式

英文关键词:convergence rate  learning law  relative degree  iterative learning control  second derivative  polynomial

中文摘要:

      为了提高收敛性和扩展学习律选项，避免高阶相对度的强约束条件，提出了在全局逆Lipschitz条件下的二阶导数迭代学习控制。利用范数理论证明了算法的收敛性。由于二阶导数对噪音敏感，采用多项式滤波避免了振荡，利于工程应用。仿真结果表明，该算法是有效的，应用于不同对象时提高了收敛速度，且精度满足工程应用。

英文摘要:

      To improve convergence rate and extend the item of learning law,and to avoid the strong constraint of higher order relative degree,iterative learning control with second derivative is proposed in the conditions of globe inverse Lipschitz.The algorithmic convergence is proved by norm theory.Because second derivative is sensitive to noise,polynomial smoothing algorithm is used to avoid oscillation,which is beneficial to engineering.Simulation shows that the algorithm is valid,convergence rate is improved in some objects,and the algorithmic precision meets the demand of engineering application.