Input-to-state stable MPC for constrained discrete-time nonlinear systems with bounded additive uncertainties

By Daniel Limón, Teodoro Alamo, Eduardo Fernández Camacho. Proceedings of the 41st IEEE Conference on Decision and Control, 2002., 2002

355 citations recorded by the source corpus.

Subfields: Control and Systems Engineering

View Paper: https://www.semanticscholar.org/paper/57616f89ff2ea964eed2798ccf0adafd9197400a

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In Control and Systems Engineering

Citations in the corpus

Architectures for distributed and hierarchical Model Predictive Control - A review (2009, methodology): “Constraint satisfaction is guaranteed by a constraint tightening procedure similar to the one already used in [15] and in [55]; feasibility and convergence are guaranteed.”
Robust MPC of constrained nonlinear systems based on interval arithmetic (2005, background): “See for instance [22] where an ISS MPC controller is proposed.”
TUBE-BASED ROBUST NONLINEAR MODEL PREDICTIVE CONTROL1 (2007, neutral): “…approaches to robust nonlinear MPC (Magni et al., 2003), some methods guarantee robustness by tightening the constraints of the nominal problem off-line by a sufficient amount, either by exploiting the continuity (Grimm et al., 2003) or Lipschitz continuity (Marruedo et al., 2002) of the dynamics.”
Optimization over state feedback policies for robust control with constraints (2006, neutral): “One such alternative is input-to-state stability (ISS), which has already been proven to be eectiv e in the study of RHC laws with input constraints only [25,26,34], RHC laws with discontinuities and output feedback [48] and in the analysis and synthesis of RHC laws with robust constraint satisfacti”
Model predictive control: Recent developments and future promise (2014, methodology): “The problem considered in Marruedo et al. (2002) is control of the system x+ = f (x, u, w) subject to the state and control constraints x(i) ∈ X and u(i) ∈ U that have to be satisfied for all permissible realizations of the disturbance sequence.”
Learning an Approximate Model Predictive Controller With Guarantees (2018, methodology): “Contrary to Lipschitz based tightening approaches as in [26], the constraint tightening is bounded even as k approaches infinity.”