Enlarging the terminal region of nonlinear model predictive control using the support vector machine method
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Publication:857150
DOI10.1016/j.automatica.2006.02.023zbMath1135.93349OpenAlexW2035303393MaRDI QIDQ857150
Publication date: 14 December 2006
Published in: Automatica (Search for Journal in Brave)
Full work available at URL: https://doi.org/10.1016/j.automatica.2006.02.023
Learning and adaptive systems in artificial intelligence (68T05) Discrete-time control/observation systems (93C55) Asymptotic stability in control theory (93D20)
Related Items (4)
Getting a suitable terminal cost and maximizing the terminal region for MPC ⋮ Distributed model predictive control of dynamically decoupled systems with coupled cost ⋮ Multidimensional Taylor network optimal control of MIMO nonlinear systems without models for tracking by output feedback ⋮ Networked min–max model predictive control of constrained nonlinear systems with delays and packet dropouts
Cites Work
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- A quasi-infinite horizon nonlinear model predictive control scheme with guaranteed stability
- Enlargement of polytopic terminal region in NMPC by interpolation and partial invariance.
- Stability regions for constrained nonlinear systems and their functional characterization via support-vector-machine learning
- A receding-horizon regulator for nonlinear systems and a neural approximation
- Constrained model predictive control: Stability and optimality
- Receding horizon control of nonlinear systems
- Robust receding horizon control of constrained nonlinear systems
- A stability theory for constrained dynamic systems with applications to electric power systems
- A stabilizing model-based predictive control algorithm for nonlinear systems
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