An Accelerating Iterative Learning Control Based on an Adjustable Learning Interval

Abstract EN

An iterative learning control algorithm with an adjustable interval is proposed for nonlinear systems to accelerate the convergence rate of iterative learning control.

For λ-norm, the monotonic convergence of ILC was analyzed, and the corresponding convergence conditions were obtained.

The results showed that the convergence rate was mainly determined by the controlled object, the control law gain, the correction factor, and the iteration interval size and that the control law gain was corrected in real time in the modified interval and the modified interval shortened as the number of iterations increased, further accelerating the convergence.

The numerical simulation shows the effectiveness of the proposed method.