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Published in:
2016 | OriginalPaper | Chapter
26. Steel Liquid Level Tracking Via Iterative Learning with Extended Error Information
Author : Yunzhong Song
Published in: Proceedings of the 2015 Chinese Intelligent Systems Conference
Publisher: Springer Berlin Heidelberg
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Abstract
This paper aims to improve the steel liquid level control quality via iterative learning control (ILC) with extended error information. The ILC is one kind of type P iterative learning control, and besides the forgetting factor and the on-off switching action, error information was further extended on account of introduction of the just past and the second past cycles error signals. Results demonstrated that, the control quality can still be improved even under the model uncertainties, periodic bulging disturbances, the measuring noises, as well as the input signal error, the state error and the output error can be guaranteed to be ultimately bounded. Simulation results were provided to clarify the suggested idea further.