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

Zhu, Quanmin; Warwick, K.

doi:10.1177/095965180221600305

Authors

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
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.