文章基本信息

标题：Training Neural Networks for Plant Estimation, Control and Disturbance Rejection
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作者：Henry Kotzé ; Herman Kamper ; Hendrik W. Jordaan 等
期刊名称：IFAC PapersOnLine
印刷版ISSN：2405-8963
出版年度：2020
卷号：53
期号：2
页码：1664-1670
DOI：10.1016/j.ifacol.2020.12.2228
语种：English
出版社：Elsevier
摘要：AbstractNeural networks are used in control systems to combat difficulties which nonlinear and linear controllers struggle to compensate for, such as environmental and model uncertainties. Neural networks have shown promising results as controllers or estimators of these uncertainties. However, few studies expand on important aspects on using and training a neural network, such as the dataset, input and output pairs, and the training of the different controllers and estimators. In this paper, a dataset used for neural controllers and estimators are presented which contains more complexity than that of the expected test environment. The training of different neural controllers and estimators are presented: estimators for the forward dynamics and disturbances, a feedback controller, a feedback linearisation controller and a disturbance rejection controller. For each neural component, the input and output pairs are presented with results of them performing in a test environment. From these results it was evident that through the use of the proposed dataset and training method the neural networks succeeded in fulfilling its role in the control architectures.
关键词：KeywordsNeuralfuzzy adaptive controlAdaptive observer designNonlinear adaptive control