控制时滞系统的遗忘因子迭代学习算法研究

提出遗忘因子是关于迭代次数的函数,简化了传统遗忘因子迭代学习控制算法的收敛条件,并给出了收敛性分析.将改进收敛条件的遗忘因子迭代学习控制算法应用于一类带控制时滞的线性系统,给出了仿真实例.仿真结果表明,在改进的收敛条件下,合理地选择遗忘因子函数,带遗忘因子的PD型迭代学习控制算法在研究的控制时滞线性系统应用之下具有一定的有效性和优越性.

Iterative learning algorithm research with forgetting factor for systems with control delay

In this paper, the thought that the forgetting factor is a function of the number of iterations is proposed, which has simplified the convergence conditions of the traditional iterative learning control algorithm with forgetting factor and its convergence analysis is given. Then, the iterative learning control algorithm with forgetting factor whose convergence conditions are improved is applied to a class of linear systems with control delay, and simulation examples are given. Simulation results show that, under the improvements of the convergence conditions and the reasonable choice of forgetting factor function, the PD-type iterative learning control algorithm with forgetting factor applied to the linear systems with control delay in this paper has effectiveness and superiority.