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A neural network enhanced generalised minimum variance self tuning PID control algorithm for complex dynamic systems

Zhu, Quanmin; Warwick, K.

Authors

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Quan Zhu Quan.Zhu@uwe.ac.uk
Professor in Control Systems

K. Warwick



Citation

Zhu, Q., & Warwick, K. (2002). A neural network enhanced generalised minimum variance self tuning PID control algorithm for complex dynamic systems. Proceedings of the Institution of Mechanical Engineers, Part I: Journal of Systems and Control Engineering, 216(3), 265-273. https://doi.org/10.1177/095965180221600305

Journal Article Type Article
Publication Date May 1, 2002
Journal Proceedings of the Institution of Mechanical Engineers, Part I: Journal of Systems and Control Engineering
Print ISSN 0959-6518
Publisher SAGE Publications
Peer Reviewed Peer Reviewed
Volume 216
Issue 3
Pages 265-273
DOI https://doi.org/10.1177/095965180221600305
Keywords neuro PID controller, complex dynamic plants, self-tuning control
Public URL https://uwe-repository.worktribe.com/output/1078050
Publisher URL http://pii.sagepub.com/content/216/3/265.abstract
Additional Information Additional Information : This work represents a fundamental concept development in neural network enhanced control system design. Particularly the significant step of simplification of design of PID controllers for nonlinear dynamic plants.


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