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

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Non-minimal state space model-based continuous-time model predictive control with constraints. / Wang, Liuping; Young, Peter C.; Gawthrop, Peter et al.
In: International Journal of Control, Vol. 82, No. 6, 06.2009, p. 1122-1137.

Research output: Contribution to Journal/Magazine › Journal article › peer-review

Harvard

Wang, L, Young, PC, Gawthrop, P & Taylor, CJ 2009, 'Non-minimal state space model-based continuous-time model predictive control with constraints', International Journal of Control, vol. 82, no. 6, pp. 1122-1137. https://doi.org/10.1080/00207170802474694

APA

Wang, L., Young, P. C., Gawthrop, P., & Taylor, C. J. (2009). Non-minimal state space model-based continuous-time model predictive control with constraints. International Journal of Control, 82(6), 1122-1137. https://doi.org/10.1080/00207170802474694

Vancouver

Wang L, Young PC, Gawthrop P, Taylor CJ. Non-minimal state space model-based continuous-time model predictive control with constraints. International Journal of Control. 2009 Jun;82(6):1122-1137. doi: 10.1080/00207170802474694

Author

Wang, Liuping ; Young, Peter C. ; Gawthrop, Peter et al. / Non-minimal state space model-based continuous-time model predictive control with constraints. In: International Journal of Control. 2009 ; Vol. 82, No. 6. pp. 1122-1137.

Bibtex

@article{c1f51450fd534819be21b95c4c3fe3dc,
title = "Non-minimal state space model-based continuous-time model predictive control with constraints",
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.",
keywords = "Predictive control, continuous time systems, non-minimal state space realization, multivariable systems, Laguerre functions",
author = "Liuping Wang and Young, {Peter C.} and Peter Gawthrop and Taylor, {C. James}",
year = "2009",
month = jun,
doi = "10.1080/00207170802474694",
language = "English",
volume = "82",
pages = "1122--1137",
journal = "International Journal of Control",
issn = "0020-7179",
publisher = "Taylor and Francis Ltd.",
number = "6",

}

RIS

TY - JOUR

T1 - Non-minimal state space model-based continuous-time model predictive control with constraints

AU - Wang, Liuping

AU - Young, Peter C.

AU - Gawthrop, Peter

AU - Taylor, C. James

PY - 2009/6

Y1 - 2009/6

N2 - 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.

AB - 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.

KW - Predictive control

KW - continuous time systems

KW - non-minimal state space realization

KW - multivariable systems

KW - Laguerre functions

U2 - 10.1080/00207170802474694

DO - 10.1080/00207170802474694

M3 - Journal article

VL - 82

SP - 1122

EP - 1137

JO - International Journal of Control

JF - International Journal of Control

SN - 0020-7179

IS - 6

ER -