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Input-to-state stability: an unifying framework for robust model predictive control
- Source :
- Nonlinear Model Predictive Control ISBN: 9783642010934
- Publication Year :
- 2009
- Publisher :
- Springer, 2009.
-
Abstract
- This paper deals with the robustness of Model Predictive Controllers for constrained uncertain nonlinear systems. The uncertainty is assumed to be modeled by a state and input dependent signal and a disturbance signal. The framework used for the analysis of the robust stability of the systems controlled by MPC is the wellknown Input-to-State Stability. It is shown how this notion is suitable in spite of the presence of constraints on the system and of the possible discontinuity of the control law.
- Subjects :
- Mathematical optimization
Robust Nonlinear Model Predictive Control
Input-to-State Stability
Robust Constraint Satisfaction
Constraint satisfaction
Nonlinear control
Nonlinear system
Model predictive control
Settore ING-INF/04 - Automatica
Control theory
Reachability
Robustness (computer science)
Minification
Robust control
Mathematics
Subjects
Details
- Language :
- English
- ISBN :
- 978-3-642-01093-4
- ISBNs :
- 9783642010934
- Database :
- OpenAIRE
- Journal :
- Nonlinear Model Predictive Control ISBN: 9783642010934
- Accession number :
- edsair.doi.dedup.....57c50b72ebcfe98d56d9a12ada0c87f7