Robustness bound for receding horizon finite memory control: Lyapunov–Krasovskii approach

DOI10.1080/00207179.2012.669849zbMath1282.93090OpenAlexW2134738315MaRDI QIDQ5745595

Choon Ki Ahn

Publication date: 30 January 2014

Published in: International Journal of Control (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1080/00207179.2012.669849

zbMATH Keywords

uncertain systemslinear matrix inequalityrobust stabilisationrobustness boundreceding horizon finite memory control

Mathematics Subject Classification ID

Sensitivity (robustness) (93B35) Linear systems in control theory (93C05) Adaptive or robust stabilization (93D21) Control/observation systems governed by ordinary differential equations (93C15)

Related Items (2)

Receding horizon robust control for nonlinear systems based on linear differential inclusion of neural networks ⋮ A new solution to the inducedl_∞finite impulse response filtering problem based on two matrix inequalities

Uses Software

• LMI toolbox

Cites Work

• On the robustness of LQ regulators
• Allowable parameter variations and robustness recovery in LQG regulators
• On the stability robustness of the continuous-time LQG optimal control
• Adaptive continuous-time linear quadratic Gaussian control
• A receding horizon Kalman FIR filter for discrete time-invariant systems
• A receding horizon Kalman FIR filter for linear continuous-time systems
• Receding-horizon unbiased FIR filters for continuous-time state-space models without a priori initial state information
• FIR filters and recursive forms for continuous time-invariant state-space models
• Receding Horizon Finite Memory Controls for Output Feedback Controls of State–Space Systems
• A receding horizon unbiased FIR for discrete-time state space models

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