区间可调节的变增益加速迭代学习控制

为加快迭代学习控制律的收敛速度,针对线性时不变系统,以P型、D型学习律为例,提出了区间可调节的、具有指数加速、含反馈信息的迭代学习控制算法。首先,根据每次学习效果,确定下一次迭代需要修正的区间并在该区间内修正控制律增益;其次,分析了所提算法的收敛性并给出其收敛条件;最后,理论结果表明收敛速度主要取决于被控对象、控制律增益、修正指数和学习区间的大小。相同仿真条件下,与传统算法相比,所提算法具有更快的收敛速度。

Accelerated iterative learning control algorithm with variable gain and adjustment of learning interval

In order to accelerate the convergence speed of the iterative learning control law, taking the P type and D type learning laws as examples, an acceleration correction algorithm with variable gain and adjustment of learning interval is proposed for the linear time invariant system. First of all, the modified interval in the next iteration is determined based on the learning effects, and the control law gain is modified in the interval. Then, the convergence of the proposed algorithm is analyzed and the convergence condition is presented. Finally, analysis results show that the convergence speed mainly depends on the system state, the learning gain, the correction exponential and the learning interval. In the same simulation condition, the proposed algorithm has a faster convergence speed compared with the traditional algorithms.