@article{eprints1208,
            year = {2011},
           title = {Robust model predictive control with integral sliding mode in continuous-time sampled-data nonlinear systems},
           pages = {556 --570},
          number = {3},
           month = {March},
          volume = {56},
          author = {Matteo Rubagotti and Davide Martino Raimondo and Antonella Ferrara and Lalo Magni},
       publisher = {IEEE },
         journal = {IEEE Transactions on Automatic Control },
             url = {http://eprints.imtlucca.it/1208/},
        abstract = {This paper proposes a control strategy for nonlinear constrained continuous-time uncertain systems which combines robust model predictive control (MPC) with sliding mode control (SMC). In particular, the so-called Integral SMC approach is used to produce a control action aimed to reduce the difference between the nominal predicted dynamics of the closed-loop system and the actual one. In this way, the MPC strategy can be designed on a system with a reduced uncertainty. In order to prove the stability of the overall control scheme, some general regional input-to-state practical stability results for continuous-time systems are proved.},
        keywords = {closed-loop system; continuous-time sampled-data nonlinear uncertain systems; input-to-state practical stability; integral sliding mode control; robust model predictive control; stability; closed loop systems; continuous time systems; nonlinear control systems; predictive control; robust control; sampled data systems; uncertain systems; variable structure systems}
}