eprintid: 9102
rev_number: 2
eprint_status: archive
userid: 1
dir: disk0/00/00/91/02
datestamp: 2023-11-09 16:21:03
lastmod: 2023-11-09 16:21:03
status_changed: 2023-11-09 16:14:16
type: conference_item
metadata_visibility: show
creators_name: Hasan, E.
creators_name: Ibrahim, R.
creators_name: Bingi, K.
creators_name: Hassan, S.M.
creators_name: Gilani, S.F.
title: Real-Time model predictive control for nonlinear gas pressure process plant
ispublished: pub
keywords: Controllers; Identification (control systems); Image processing; Model predictive control; Predictive control systems; Religious buildings; Robustness (control systems); State space methods, Gas pressures; Industrial process control; Nonlinear process; PI Controller; Process plants; State - space models, Process control
note: cited By 1; Conference of 5th IEEE International Conference on Signal and Image Processing Applications, ICSIPA 2017 ; Conference Date: 12 September 2017 Through 14 September 2017; Conference Code:132915
abstract: Nonlinear behaviour of the systems happens to be a common problem in industrial processes. They cause a large amount of time, resources and efforts to be utilized in order to deal with them. A Major hurdle in Nonlinear Industrial Processes is system modeling. Due to this reason, several methods and techniques have been designed and developed in order to improve the overall control performance in industrial process control. Model based controllers have been developed and implemented on various applications with promising results. Their main benefit is they can identify and tune unknown system parameters in real-Time. This paper focuses on real-Time controller development and its implementation on Gas Pressure Process Plant using MPC. MPC is considered to be one of the robust and effective controllers due to impressive control performance in different applications previously. MPC makes use of a model for system identification and based upon that, it can dynamically send next control move for the system. This research work incorporates State-Space Model for unknown system-parameter identification. The identified parameters will be utilized by MPC for control law development. The proposed methodology is validated by real-Time experimental results on the aforementioned system. © 2017 IEEE.
date: 2017
publisher: Institute of Electrical and Electronics Engineers Inc.
official_url: https://www.scopus.com/inward/record.uri?eid=2-s2.0-85041376704&doi=10.1109%2fICSIPA.2017.8120625&partnerID=40&md5=259cb4768fc19737646a578a5df495f1
id_number: 10.1109/ICSIPA.2017.8120625
full_text_status: none
publication: Proceedings of the 2017 IEEE International Conference on Signal and Image Processing Applications, ICSIPA 2017
pagerange: 299-303
refereed: TRUE
isbn: 9781509055593
citation:   Hasan, E. and Ibrahim, R. and Bingi, K. and Hassan, S.M. and Gilani, S.F.  (2017) Real-Time model predictive control for nonlinear gas pressure process plant.  In: UNSPECIFIED.