In this paper, iterative learning control using output data, which are more advanced than the relative degree of the system, is being investigated. It is known that the output error can be made zero with the conventional iterative learning control in which the input is updated with the output data advanced by the relative degree. However, the input can become too large for nonminimum phase syst...

Monotonic convergence of iterative learning control systems with variable pass length Thomas Seel, Thomas Schauer & Jörg Raisch To cite this article: Thomas Seel, Thomas Schauer & Jörg Raisch (2017) Monotonic convergence of iterative learning control systems with variable pass length, International Journal of Control, 90:3, 393-406, DOI: 10.1080/00207179.2016.1183172 To link to this article: ht...

This paper presents an approach to the use of neural networks to improve iterative learning control performance. The neural networks are used to estimate learning gain of an iterative learning law and to store the learned control input prooles for diierent reference trajectories. A neural network of piecewise linear approximation is presented to eeectively identify the system dynamics, and the ...

In this paper, we provide uniform design and analysis framework for iterative learning control of a class of impulsive first-order distributed parameter systems in the time domain. In particular, P-type and D-type iterative learning controls with initial state learning are considered. We present convergence results for open-loop iterative learning schemes in the sense of the Lp-norm and λ-norm,...

This paper presents an adaptive iterative learning control scheme that is applicable to a class of nonlinear systems. The control scheme guarantees system stability and boundedness by using the feedback controller coupled with the fuzzy compensator and achieves precise tracking by using the iterative learning rules. In the feedback plus fuzzy compensator unit, the feedback control part stabiliz...