@incollection{eprints533,
           title = {A decomposition algorithm for feedback min-max model predictive control},
            year = {2005},
           pages = {5126--5131},
         address = {12th-15th Dec. 2005 },
       booktitle = {Decision and Control and European Control Conference},
       publisher = {IEEE},
         journal = {Decision and Control and European Control Conf.},
          author = {David Mu{\~n}oz de la Pe{\~n}a and Alberto Bemporad and Teodoro Alamo},
        keywords = {Optimization algorithms; Predictive control for linear systems; Uncertain systems},
             url = {http://eprints.imtlucca.it/533/},
        abstract = {An algorithm for solving feedback min-max model predictive control for discrete time uncertain linear systems with constraints is presented in the paper. The algorithm solves the corresponding multi-stage min-max linear optimization problem. It is based on applying recursively a decomposition technique to solve the min-max problem via a sequence of low complexity linear programs. It is proved that the algorithm converges to the optimal solution in finite time. Simulation results are provided to compare the proposed algorithm with other approaches. }
}