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. ISSN 0020-7179

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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.

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Journal Article
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International Journal of Control
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Deposited On:
18 May 2010 12:27
Last Modified:
21 Nov 2022 20:01