随机分布控制系统的自适应迭代学习控制

Iterative Learning Control of Stochastic Distribution Control Systems

介绍输出概率密度函数(PDF)常规的迭代学习控制(ILC)的收敛条件,并利用此条件设计相应的迭代学习律。主要讨论如何解决输出PDF迭代学习控制(ILC)中的过迭代,收敛速度等问题。以离散输出概率密度函数(PDF)控制模型为基础,介绍了直接迭代学习控制算法收敛的必要条件,提出自适应的迭代学习参数调节方法和避免过迭代的迭代结束条件,这些措施能够保证输出PDF的迭代控制收敛且具有较快的收敛速度。仿真结果表明,输出PDF的自适应迭代学习控制具有较快的收敛速度,而学习终止条件能很好地避免过迭代。

The convergence conditions of the iterative learning control （ ILC ） are discussed, in the output probability density function （PDF） systems. The problems to avoid over iteration and to improve the convergence speed of this ILC systems are discussed. Based on the discrete control model of output PDF systems, the convergence conditions of direct ILC are introduced. The adaptive iterative learn- ing rate according to the accumulated tracking error is proposed and the termination criterion of the iteration is given, which can ensure the convergence and the convergence speed. The simulation results show the effectiveness of the adaptive ILC algorithms and termination criterion for the output PDF control systems.