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Non-minimal state space model-based continuous-time model predictive control with constraints

Wang, Liuping and Young, Peter C. and Gawthrop, Peter and Taylor, C. James (2009) Non-minimal state space model-based continuous-time model predictive control with constraints. International Journal of Control, 82 (6). pp. 1122-1137.

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Abstract

This paper proposes a model predictive control scheme based on a non-minimal state-space (NMSS) structure. Such a combination yields a continuous-time state-space model predictive control system that permits hard constraints to be imposed on both plant input and output variables, whilst using NMSS output-feedback without the need for an observer. A comparison between the NMSS and observer-based approaches using Monte Carlo uncertainty analysis shows that the former design is considerably less sensitive to plant-model mismatch than the latter. Through simulation studies, the paper also investigates the role of the implementation filter in noise attenuation, disturbance rejection and robustness of the closed-loop predictive control system. The results show that the filter poles become a subset of the closed-loop poles and this provides a straightforward method of tuning the closed-loop performance to achieve a reasonable balance between speed of response, disturbance rejection, measurement noise attenuation and robustness.

Item Type: Article
Journal or Publication Title: International Journal of Control
Uncontrolled Keywords: Predictive control ; continuous time systems ; non-minimal state space realization ; multivariable systems ; Laguerre functions
Subjects: T Technology > TA Engineering (General). Civil engineering (General)
Departments: Faculty of Science and Technology > Lancaster Environment Centre
Faculty of Science and Technology > Engineering
ID Code: 33441
Deposited By: Dr C. James Taylor (Engineering)
Deposited On: 18 May 2010 13:27
Refereed?: Yes
Published?: Published
Last Modified: 24 Jan 2014 05:18
Identification Number:
URI: http://eprints.lancs.ac.uk/id/eprint/33441

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