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Input-To-State Stabilization of Low-Complexity Model Predictive Controllers for Linear Systems


G. Schildbach, M.N. Zeilinger, M. Morari, C. Jones

Conference on Decision and Control (CDC), Orlando (FL), United States, vol. 50, pp. 482-488

Research on sub-optimal Model Predictive Control (MPC) has led to a variety of optimization methods based on explicit or online approaches, or combinations thereof. Its foremost aim is to guarantee essential controller properties, i.e. recursive feasibility, stability, and robustness, at reduced and predictable computational cost, i.e. computation time and storage space. This paper shows how the input sequence of any (not necessarily stabilizing) sub-optimal controller and the shifted input sequence from the previous time step can be used in an optimal combination, which is easy to determine online, in order to guarantee input-to-state stability (ISS) for the closed-loop system. The presented method is thus able to stabilize a wide range of existing sub-optimal MPC schemes that lack a formal stability guarantee, if they can be considered as a continuous map from the state space to the space of feasible input sequences.


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% Autogenerated BibTeX entry
@InProceedings { SchEtal:2011:IFA_3899,
    author={G. Schildbach and M.N. Zeilinger and M. Morari and C. Jones},
    title={{Input-To-State Stabilization of Low-Complexity Model
	  Predictive Controllers for Linear Systems}},
    booktitle={Conference on Decision and Control (CDC)},
    address={Orlando (FL), United States},
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