An improved structure for model predictive control using non-minimal state space realisation

This paper describes a new method for the design of model predictive control (MPC) using non-minimal 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 ob...

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Bibliographic Details
Published in:Journal of process control 2006-04, Vol.16 (4), p.355-371
Main Authors: Wang, Liuping, Young, Peter C.
Format: Article
Language:English
Subjects:
Discrete time systems
Model predictive control
Non-minimal state space model
Quadratic programming
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Summary:This paper describes a new method for the design of model predictive control (MPC) using non-minimal 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, when there is no model/plant mismatch, the paper shows that the system output constraints can be realised in the proposed approach. Furthermore, closed-form transfer function representation of the model predictive control system enables the application of frequency response analysis tools to the nominal performance of the system.
ISSN:0959-1524
1873-2771
DOI:10.1016/j.jprocont.2005.06.016