Rubagotti, Matteo and Raimondo, Davide Martino and Ferrara, Antonella and Magni, Lalo Robust model predictive control with integral sliding mode in continuous-time sampled-data nonlinear systems. IEEE Transactions on Automatic Control , 56 (3). 556 -570. ISSN 0018-9286 (2011)Full text not available from this repository.
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.
|Projects:||This work was supported in part by the European Commission under the Project Feednetback FP7-ICT-223866 and by the Italian PRIN project “Model predictive control algorithms for artificial pancreas.”|
|Uncontrolled 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|
|Subjects:||Q Science > QA Mathematics > QA75 Electronic computers. Computer science
T Technology > TJ Mechanical engineering and machinery
|Research Area:||Computer Science and Applications|
|Depositing User:||Users 37 not found.|
|Date Deposited:||02 Mar 2012 15:30|
|Last Modified:||30 Sep 2013 12:33|
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