Generalized predictive control with dual adaptation

Yong Kuen Ho, Farouq S. Mjalli, Hak Koon Yeoh

Research output: Contribution to journalArticle

10 Citations (Scopus)

Abstract

In this work, the recursive least squares (RLS) algorithm, which traditionally was used in the generalized predictive controller (GPC) framework solely for model adaptation purposes, was extended to cater for auto-tuning of the controller. This new combination which eases the task of controller tuning, contains both model adaptation and auto-tuning capabilities within the same controller structure. Hereafter this scheme will be referred to as the adaptive-model based self-tuning generalized predictive control (AS-GPC). The variable forgetting factor recursive least squares (VFF-RLS) algorithm was selected to capture the dynamics of the process online for the purpose of model adaptation in the controller. Based on the evolution of the process dynamics given by the VFF-RLS algorithm in the form of first order plus dead time (FOPDT) model parameters, the move suppression weight for the AS-GPC was recalculated automatically at every time step based on existing single input single output (SISO) analytical tuning expressions originally used for offline tuning of constraint-free predictive controllers. Closed loop simulation on a validated transesterification reactor model, known for inherent nonlinearities, revealed the superiority of the proposed constrained control scheme in terms of servo and regulatory control as compared to the GPC with model adaptation only, the conventional GPC as well as the conventional PID controller. The tuning expressions used, although intended for constraint-free predictive controllers, yielded good results even in the constrained case.

Original languageEnglish
Pages (from-to)479-493
Number of pages15
JournalChemical Engineering Science
Volume84
DOIs
Publication statusPublished - Dec 24 2012

Fingerprint

Generalized Predictive Control
Controller
Tuning
Controllers
Least Square Algorithm
Recursive Algorithm
Auto-tuning
Self-tuning
Model
Model-based
Constrained Control
PID Controller
Dynamic Process
Transesterification
Reactor
Closed-loop
Nonlinearity
First-order

Keywords

  • Generalized predictive control
  • Nonlinear dynamics
  • Parameter identification
  • Process control
  • Recursive least squares
  • Systems engineering

ASJC Scopus subject areas

  • Chemical Engineering(all)
  • Chemistry(all)
  • Applied Mathematics
  • Industrial and Manufacturing Engineering

Cite this

Generalized predictive control with dual adaptation. / Ho, Yong Kuen; Mjalli, Farouq S.; Yeoh, Hak Koon.

In: Chemical Engineering Science, Vol. 84, 24.12.2012, p. 479-493.

Research output: Contribution to journalArticle

Ho, Yong Kuen ; Mjalli, Farouq S. ; Yeoh, Hak Koon. / Generalized predictive control with dual adaptation. In: Chemical Engineering Science. 2012 ; Vol. 84. pp. 479-493.
@article{e5f43765f85c4ce89f66c02b46b6f14e,
title = "Generalized predictive control with dual adaptation",
abstract = "In this work, the recursive least squares (RLS) algorithm, which traditionally was used in the generalized predictive controller (GPC) framework solely for model adaptation purposes, was extended to cater for auto-tuning of the controller. This new combination which eases the task of controller tuning, contains both model adaptation and auto-tuning capabilities within the same controller structure. Hereafter this scheme will be referred to as the adaptive-model based self-tuning generalized predictive control (AS-GPC). The variable forgetting factor recursive least squares (VFF-RLS) algorithm was selected to capture the dynamics of the process online for the purpose of model adaptation in the controller. Based on the evolution of the process dynamics given by the VFF-RLS algorithm in the form of first order plus dead time (FOPDT) model parameters, the move suppression weight for the AS-GPC was recalculated automatically at every time step based on existing single input single output (SISO) analytical tuning expressions originally used for offline tuning of constraint-free predictive controllers. Closed loop simulation on a validated transesterification reactor model, known for inherent nonlinearities, revealed the superiority of the proposed constrained control scheme in terms of servo and regulatory control as compared to the GPC with model adaptation only, the conventional GPC as well as the conventional PID controller. The tuning expressions used, although intended for constraint-free predictive controllers, yielded good results even in the constrained case.",
keywords = "Generalized predictive control, Nonlinear dynamics, Parameter identification, Process control, Recursive least squares, Systems engineering",
author = "Ho, {Yong Kuen} and Mjalli, {Farouq S.} and Yeoh, {Hak Koon}",
year = "2012",
month = "12",
day = "24",
doi = "10.1016/j.ces.2012.08.040",
language = "English",
volume = "84",
pages = "479--493",
journal = "Chemical Engineering Science",
issn = "0009-2509",
publisher = "Elsevier BV",

}

TY - JOUR

T1 - Generalized predictive control with dual adaptation

AU - Ho, Yong Kuen

AU - Mjalli, Farouq S.

AU - Yeoh, Hak Koon

PY - 2012/12/24

Y1 - 2012/12/24

N2 - In this work, the recursive least squares (RLS) algorithm, which traditionally was used in the generalized predictive controller (GPC) framework solely for model adaptation purposes, was extended to cater for auto-tuning of the controller. This new combination which eases the task of controller tuning, contains both model adaptation and auto-tuning capabilities within the same controller structure. Hereafter this scheme will be referred to as the adaptive-model based self-tuning generalized predictive control (AS-GPC). The variable forgetting factor recursive least squares (VFF-RLS) algorithm was selected to capture the dynamics of the process online for the purpose of model adaptation in the controller. Based on the evolution of the process dynamics given by the VFF-RLS algorithm in the form of first order plus dead time (FOPDT) model parameters, the move suppression weight for the AS-GPC was recalculated automatically at every time step based on existing single input single output (SISO) analytical tuning expressions originally used for offline tuning of constraint-free predictive controllers. Closed loop simulation on a validated transesterification reactor model, known for inherent nonlinearities, revealed the superiority of the proposed constrained control scheme in terms of servo and regulatory control as compared to the GPC with model adaptation only, the conventional GPC as well as the conventional PID controller. The tuning expressions used, although intended for constraint-free predictive controllers, yielded good results even in the constrained case.

AB - In this work, the recursive least squares (RLS) algorithm, which traditionally was used in the generalized predictive controller (GPC) framework solely for model adaptation purposes, was extended to cater for auto-tuning of the controller. This new combination which eases the task of controller tuning, contains both model adaptation and auto-tuning capabilities within the same controller structure. Hereafter this scheme will be referred to as the adaptive-model based self-tuning generalized predictive control (AS-GPC). The variable forgetting factor recursive least squares (VFF-RLS) algorithm was selected to capture the dynamics of the process online for the purpose of model adaptation in the controller. Based on the evolution of the process dynamics given by the VFF-RLS algorithm in the form of first order plus dead time (FOPDT) model parameters, the move suppression weight for the AS-GPC was recalculated automatically at every time step based on existing single input single output (SISO) analytical tuning expressions originally used for offline tuning of constraint-free predictive controllers. Closed loop simulation on a validated transesterification reactor model, known for inherent nonlinearities, revealed the superiority of the proposed constrained control scheme in terms of servo and regulatory control as compared to the GPC with model adaptation only, the conventional GPC as well as the conventional PID controller. The tuning expressions used, although intended for constraint-free predictive controllers, yielded good results even in the constrained case.

KW - Generalized predictive control

KW - Nonlinear dynamics

KW - Parameter identification

KW - Process control

KW - Recursive least squares

KW - Systems engineering

UR - http://www.scopus.com/inward/record.url?scp=84868662640&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=84868662640&partnerID=8YFLogxK

U2 - 10.1016/j.ces.2012.08.040

DO - 10.1016/j.ces.2012.08.040

M3 - Article

AN - SCOPUS:84868662640

VL - 84

SP - 479

EP - 493

JO - Chemical Engineering Science

JF - Chemical Engineering Science

SN - 0009-2509

ER -