# Adaptive iterative learning control based on IF–THEN rules and data-driven scheme for a class of nonlinear discrete-time systems

## Abstract

An adaptive iterative learning controller (ILC) is designed for a class of nonlinear discrete-time systems based on data driving control (DDC) scheme and adaptive networks called fuzzy rules emulated network (FREN). The proposed control law is derived by using DDC scheme with a compact form dynamic linearization for iterative systems. The pseudo-partial derivative of linearization model is estimated by the proposed tuning algorithm and FREN established by human knowledge of controlled plants within the format of IF–THEN rules related on input–output data set. An on-line learning algorithm is proposed to compensate unknown nonlinear terms of controlled plant, and the controller allows to change desired trajectories for other iterations. The performance of control scheme is verified by theoretical analysis under reasonable assumptions which can be held for a general class of practical controlled plants. The experimental system is constructed by a commercial DC motor current control to confirm the effectiveness and applicability. The comparison results are addressed with a general ILC scheme based on DDC.

## Keywords

Iterative learning control Data-driven control Discrete-time systems Adaptive control DC motor Neuro-fuzzy## Notes

### Acknowledgments

The author gratefully acknowledges the contributions of CINVESTAV-IPN’s research Grant 2013–2014 and Mexican Research Organization CONACyT Grant # 257253.

### Compliance with ethical standards

### Conflict of interest

Chidentree Treesatayapun declares that he has no conflict of interest.

### Ethical approval

This article does not contain any studies with human participants performed by any of the authors.

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