Model Predictive Control Design Using Non-minimal State Space Model
Abstract
This paper examines the design of modelpredictive control using non-minimal state space models, in whichthe state variables are chosen as the set of measured input andoutput variables and their past values. It shows that the proposeddesign approach avoids the use of an observer to access the stateinformation and, as a result, the disturbance rejection,particularly the system input disturbance rejection, issignificantly improved when constraints become activated. Inaddition, the paper shows that the system output constraints canbe achieved in the proposed approach, which provides a significantimprovement over the general observer based approach.
