Practical NMPC suboptimality estimates along trajectories

DOI10.1016/j.sysconle.2008.10.012zbMath1159.93013OpenAlexW2137435374MaRDI QIDQ1016590

Lars Grüne, Jürgen Pannek

Publication date: 6 May 2009

Published in: Systems \& Control Letters (Search for Journal in Brave)

Full work available at URL: https://epub.uni-bayreuth.de/5586/1/gruene_pannek_scl_2009.pdf

zbMATH Keywords

stabilitymodel predictive controlsuboptimality estimates

Mathematics Subject Classification ID

Design techniques (robust design, computer-aided design, etc.) (93B51)

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Cites Work

• Unnamed Item
• Stabilization of sampled-data nonlinear systems by receding horizon control via discrete-time approximations
• On optimality of nonlinear model predictive control
• Optimal infinite-horizon feedback laws for a general class of constrained discrete-time systems: Stability and moving-horizon approximations
• Stability margins of nonlinear receding-horizon control via inverse optimality
• Constrained model predictive control: Stability and optimality
• A receding horizon control approach to sampled-data implementation of continuous-time controllers
• Optimization Based Stabilization of Nonlinear Control Systems
• Linear nonquadratic optimal control
• Receding horizon optimal control for infinite dimensional systems
• Inverse Optimality in Robust Stabilization
• On the Infinite Horizon Performance of Receding Horizon Controllers
• A Framework for Stabilization of Nonlinear Sampled-Data Systems Based on Their Approximate Discrete-Time Models
• Relaxing Dynamic Programming
• Sufficient conditions for stabilization of sampled-data nonlinear systems via discrete-time approximations