An iterative learning controller (ILC) is an online method which exploits the information of past trials to improve the performance of the system. For a system controlled by ILC, the state, error, and ILC time histories for varying operating conditions can be recorded. This paper proposes an offline learning method using a neural network which exploits this dataset to approximate the converged ILC for a nonlinear system. The proposed method provides an approximate ILC for the first iteration based on the data collected thereby achieving a faster convergence. The efficiency of the method is tested for a nonlinear problem and results are presented.
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Neural Network Augmented Intelligent Iterative Learning Control for a Nonlinear System
Semantic Scholar · Engineering · 2020
Abstract
An iterative learning controller (ILC) is an online method which exploits the information of past trials to improve the performance of the system. For a system controlled by ILC, the state, error, and ILC time histories for varying operating conditions can be recorded. This paper proposes an offline learning method using a neural network which exploits this dataset to approximate the converged ILC for a nonlinear system. The proposed method provides an approximate ILC for the first iteration based on the data collected thereby achieving a faster convergence. The efficiency of the method is tested for a nonlinear problem and results are presented.