Skip to main content

Research Repository

See what's under the surface

Advanced Search

U-model based predictive control for nonlinear processes with input delay

Geng, Xinpeng; Zhu, Quanmin; Liu, Tao; Na, Jing

Authors

Xinpeng Geng

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

Tao Liu

Jing Na



Abstract

In this paper, a general control scheme is proposed for nonlinear dynamic processes with input delay described by different models, including polynomial models, state-space models, nonlinear autoregressive moving average with eXogenous inputs (NARMAX) models, Hammerstein or Wiener type models. To tackle the input delay and nonlinear dynamics involved with the control system design, it integrates the classical Smith predictor and a U-model based controller into a U-model based predictive control scheme, which gives a general solution of two-degree-of-freedom (2DOF) control for the set-point tracking and disturbance rejection, respectively. Both controllers are analytically designed by proposing thedesired transfer functions for the above objectives in terms of a linear system expression with the U-model, and therefore are independent of the process model for implementation. Meanwhile, the control system robust stability is analyzed in the presence of process uncertainties. To demonstrate the control performance and advantage, three examples from the literature are conducted with a user-friendly step by step procedure for the ease of understanding by readers.

Journal Article Type Article
Publication Date Mar 1, 2019
Journal Journal of Process Control
Print ISSN 0959-1524
Publisher Elsevier
Peer Reviewed Peer Reviewed
Volume 75
Pages 156-170
APA6 Citation Geng, X., Zhu, Q., Liu, T., & Na, J. (2019). U-model based predictive control for nonlinear processes with input delay. Journal of Process Control, 75, 156-170. https://doi.org/10.1016/j.jprocont.2018.12.002
DOI https://doi.org/10.1016/j.jprocont.2018.12.002
Publisher URL https://doi.org/10.1016/j.jprocont.2018.12.002
Additional Information Additional Information : This is the author's accepted manuscript. The final published version is available here: https://doi.org/10.1016/j.jprocont.2018.12.002.

Files







You might also like



Downloadable Citations

;