Abstract:

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.