State-space approach to nonlinear predictive generalized minimum variance control

M.J. Grimble, P.M. Majecki, EPSRC on the Platform Grant Project No EP/C526422/1 (Funder)

Research output: Contribution to journalArticlepeer-review

12 Citations (Scopus)
279 Downloads (Pure)

Abstract

A Nonlinear Predictive Generalized Minimum Variance (NPGMV) control algorithm is introduced for the control of nonlinear discrete-time multivariable systems. The plant model is represented by the combination of a very general nonlinear operator and also a linear subsystem which can be open-loop unstable and is represented in state-space model form. The multi-step predictive control cost index to be minimised involves both weighted error and control signal costing terms. The solution for the control law is derived in the time-domain using a general operator representation of the process. The controller includes an internal model of the nonlinear process but because of the assumed structure of the system the state observer is only required to be linear. In the asymptotic case, where the plant is linear, the controller reduces to a state-space version of the well known GPC controller.
Original languageEnglish
Pages (from-to)1529-1547
Number of pages18
JournalInternational Journal of Control
Volume83
Issue number8
DOIs
Publication statusPublished - 17 Jun 2010

Keywords

  • state-space
  • predictive
  • nonlinear
  • optimal
  • minimum variance
  • transport delay

Fingerprint

Dive into the research topics of 'State-space approach to nonlinear predictive generalized minimum variance control'. Together they form a unique fingerprint.

Cite this