Robust predictive feedback control for constrained systems

Leonardo Giovanini, Michael Grimble

Research output: Contribution to journalArticle

5 Citations (Scopus)
66 Downloads (Pure)

Abstract

A new method for the design of predictive controllers for SISO systems is presented. The proposed technique allows uncertainties and constraints to be concluded in the design of the control law. The goal is to design, at each sample instant, a predictive feedback control law that minimizes a performance measure and guarantees of constraints are satisfied for a set of models that describes the system to be controlled. The predictive controller consists of a finite horizon parametric-optimization problem with an additional constraint over the manipulated variable behavior. This is an end-constraint based approach that ensures the exponential stability of the closed-loop system. The inclusion of this additional constraint, in the on-line optimization algorithm, enables robust stability properties to be demonstrated for the closed-loop system. This is the case even though constraints and disturbances are present. Finally, simulation results are presented using a nonlinear continuous stirred tank reactor model.
Original languageEnglish
Pages (from-to)407-422
Number of pages16
JournalInternational Journal of Control, Automation and Systems
Volume2
Issue number4
Publication statusPublished - 2004

Keywords

  • predictive control
  • parametric optimization
  • multi-objective optimization
  • control systems
  • predictive feedback control

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