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An improved structure for model predictive control using non-minimal state space realisation
Authors:Liuping Wang  Peter C. Young
Affiliation:aSchool of Electrical and Computer Engineering, Royal Melbourne Institute of Technology (RMIT) University, Melbourne VIC 3000, Australia;bCentre for Research on Environmental Systems and Statistics, Lancaster University, Lancaster LA1 4 YQ, UK
Abstract:
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.
Keywords:Discrete time systems   Model predictive control   Non-minimal state space model   Quadratic programming
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