Neural Network Augmented Intelligent Iterative Learning Control for a Nonlinear System
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.
Recommended Citation
D. Lakshmidevinivas et al., "Neural Network Augmented Intelligent Iterative Learning Control for a Nonlinear System," Proceedings of the International Joint Conference on Neural Networks, Jul 2020.
The definitive version is available at https://doi.org/10.1109/IJCNN48605.2020.9207260
Meeting Name
International Joint Conference on Neural Networks, IJCNN 2020 (2020: Jul. 19-24, Glasgow, UK)
Department(s)
Mechanical and Aerospace Engineering
Keywords and Phrases
iterative learning control; neural network; offline learning
International Standard Book Number (ISBN)
978-172816926-2
Document Type
Article - Conference proceedings
Document Version
Citation
File Type
text
Language(s)
English
Publication Date
24 Jul 2020
Comments
National Aeronautics and Space Administration, Grant NNX15AM51A