ArticleACS omega2022
Learning of Iterative Learning Control for Flexible Manufacturing of Batch Processes.
Article in ACS omega, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Authors and funding
6 authors.
Funding
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Abstract
Flexible manufacturing as an essential component of smart manufacturing implements the customized production mode, thereby requesting fast controller adaptation for producing different goods but still with high precision. This problem becomes even more acute for batch processes. Here we present a solution called learning of iterative learning control (ILC) based on neural networks. It is able to recommend control parameters for ILC controllers accordingly, so as to yield fast tracking error convergence and smaller steady-state error for disparate set-point profiles, which is deemed an abstraction of different production needs. The method substantially outperforms a benchmark ILC on a variety of systems and cases, thereby showing its potential for deployment in the industrial Internet of Things.
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Registered trials
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