Model Predictive Control Design Using Non-minimal State Space Model

This paper examines the design of model predictive control using nonminimal state space models, in which the state variables are chosen as the set of measured input and output variables and their past values. It shows that the proposed design approach avoids the use of an observer to access the state information and, as a result, the disturbance rejection, particularly the system input disturbance rejection, is significantly improved when constraints become activated. In addition, the paper shows that the system output constraints can be achieved in the proposed approach, which provides a significant improvement over the general observer based approach.Copyright c ©2005 IFAC