Enhancing the performance of intelligent control systems in the face of higher levels of complexity and uncertainty


Modern advances in technology have led to more complex manufacturing processes whose success centres on the ability to control these processes with a very high level of accuracy. Plant complexity inevitably leads to poor models that exhibit a high degree of parametric or functional uncertainty. The situation becomes even more complex if the plant to be controlled is characterised by a multivalued function or even if it exhibits a number of modes of behaviour during its operation. Since an intelligent controller is expected to operate and guarantee the best performance where complexity and uncertainty coexist and interact, control engineers and theorists have recently developed new control techniques under the framework of intelligent control to enhance the performance of the controller for more complex and uncertain plants. These techniques are based on incorporating model uncertainty. The newly developed control algorithms for incorporating model uncertainty are proven to give more accurate control results under uncertain conditions. In this paper, we survey some approaches that appear to be promising for enhancing the performance of intelligent control systems in the face of higher levels of complexity and uncertainty.

Publication DOI: https://doi.org/10.1504/IJMIC.2011.040076
Divisions: College of Engineering & Physical Sciences > School of Informatics and Digital Engineering > Mathematics
College of Engineering & Physical Sciences > Systems analytics research institute (SARI)
Uncontrolled Keywords: functional uncertainty,inverse control,adaptive control,mixture density network,MDN,multiple models,neural networks,intelligent control,plant complexity,complex manufacturing
Publication ISSN: 1746-6180
Full Text Link: http://www.inde ... php?artid=40076
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PURE Output Type: Article
Published Date: 2011
Authors: Herzallah, Randa (ORCID Profile 0000-0001-9128-6814)



Version: Accepted Version

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