Projects per year
Abstract
A relatively simple approach to nonlinear predictive generalised minimum variance (NPGMV) control is introduced for nonlinear discretetime multivariable systems. The system is represented by a combination of a stable nonlinear subsystem where no structure is assumed and a linear subsystem that may be unstable and modelled in polynomial matrix form. The multistep predictive control cost index to be minimised involves both weighted error and control signal costing terms. The NPGMV control law involves an assumption on the choice of costfunction weights to ensure the existence of a stable nonlinear closedloop operator. A valuable feature of the control law is that in the asymptotic case, where the plant is linear, the controller reduces to a polynomial matrix version of the well known generalised predictive control (GPC) controller. In the limiting case when the plant is nonlinear and the costfunction is single step the controller becomes equal to the polynomial matrix version of the socalled nonlinear generalised minimum variance controller. The controller can be implemented in a form related to a nonlinear version of the Smith predictor but unlike this compensator a stabilising control law can be obtained for openloop unstable processes.
Original language  English 

Pages (fromto)  411424 
Number of pages  14 
Journal  IET Control Theory and Applications 
Volume  4 
Issue number  3 
DOIs  
Publication status  Published  1 Mar 2010 
Keywords
 polynomial systems
 optimal
 predictive
 nonlinear
 minimum variance
 transport delays
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Projects
 2 Finished

INDUSTRIAL NONLINEAR CONTROL AND REALTIME APPLICATIONS
Grimble, M., Katebi, R. & Ordys, A.
EPSRC (Engineering and Physical Sciences Research Council)
1/05/05 → 30/04/10
Project: Research
Impacts

Practical Nonlinear Controllers for Industrial Applications
Michael Grimble (Participant) & M Katebi (Participant)
Impact: Impact  for External Portal › Economic and commerce, Professional practice, training and standards, Environment and sustainability  natural world and built environment