Distributed model predictive control -- recursive feasibility under inexact dual optimization
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Publication:1737767
DOI10.1016/j.automatica.2018.12.037zbMath1415.93108OpenAlexW2908578034WikidataQ128563854 ScholiaQ128563854MaRDI QIDQ1737767
Frank Allgöwer, Matthias A. Müller, Johannes Köhler
Publication date: 24 April 2019
Published in: Automatica (Search for Journal in Brave)
Full work available at URL: https://doi.org/10.1016/j.automatica.2018.12.037
large scale systemsmodel predictive controlcontrol of constrained systemsdistributed dual optimization
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Cites Work
- Distributed synthesis and stability of cooperative distributed model predictive control for linear systems
- Cooperative distributed model predictive control
- On linear convergence of a distributed dual gradient algorithm for linearly constrained separable convex problems
- Distributed model predictive control -- recursive feasibility under inexact dual optimization
- Nonlinear model predictive control. Theory and algorithms
- A One-Step Approach to Computing a Polytopic Robust Positively Invariant Set
- On Feasibility, Stability and Performance in Distributed Model Predictive Control
- Suboptimal model predictive control (feasibility implies stability)
- Application of a Smoothing Technique to Decomposition in Convex Optimization
- An adaptive constraint tightening approach to linear model predictive control based on approximation algorithms for optimization
- Systems with persistent disturbances: Predictive control with restricted constraints
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