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

Research output: Contribution to Journal/MagazineJournal articlepeer-review

<mark>Journal publication date</mark>06/2009
<mark>Journal</mark>International Journal of Control
Issue number6
Number of pages16
Pages (from-to)1122-1137
Publication StatusPublished
<mark>Original language</mark>English


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.