摘要
迭代学习控制(ILC)利用系统的重复性不断改进控制性能。本文讨论一类具有扰动的非线性、时变系统高阶迭代学习控制算法及其迭代学习收敛的充分条件,并与D型迭代学习算法相比,讨论典型PD高阶ILC算法的收敛速度。仿真结果证实高阶ILC算法具有更快的收敛速度,并且当系统满足收敛条件、不确定项及输出扰动项有界时迭代学习收敛。
The basic idea of iterative learning control （ILC） is to make use of the repetitiveness within the system to achieve a better performance. High-order ILC convergence sufficient condition is provided for a non-linear time varying dynamic system with uncertainties and disturbances . Convergence speed of the typical PD high-order ILC scheme is also discussed in comparison with D-type ILC algorithm. Simulation examples are provided to illustrate that high-order ILC scheme can be better than D-type ILC in terms of convergence speed and the convergence is guaranteed if the proposed conditions are satisfied and items of uncertainties and disturbances are bounded.
出处
《模式识别与人工智能》
EI
CSCD
北大核心
2006年第4期450-454,共5页
Pattern Recognition and Artificial Intelligence
关键词
迭代学习控制(ILC)
收敛条件
收敛速度
Iterative Learning Control （ILC）, Convergence Condition, Convergence Rate
