Neural Network Augmented Intelligent Iterative Learning Control for a Nonlinear System
2020 INTERNATIONAL JOINT CONFERENCE ON NEURAL NETWORKS (IJCNN)(2020)
摘要
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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关键词
iterative learning control, neural network, offline learning
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