Robustness of multipredictor adaptive regulators: MUSMAR

DOI10.1016/0005-1098(89)90095-2zbMath0694.93055OpenAlexW2055278181MaRDI QIDQ909624

João M. Lemos, Giovanni Zappa, Mosca, Edoardo

Publication date: 1989

Published in: Automatica (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/0005-1098(89)90095-2

zbMATH Keywords

robustnessunmodelled dynamicsLQ self-tuning regularmultipredictor informationMUSMAR algorithmplant structural uncertainties

Mathematics Subject Classification ID

Sensitivity (robustness) (93B35) Adaptive control/observation systems (93C40) Optimal stochastic control (93E20)

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Long-range adaptive control with input constraints ⋮ The method of averaged models for discrete-time adaptive systems ⋮ Predictive adaptive control of plants with online structural changes based on multiple models ⋮ Feedforward adaptive control of the Bispectral Index of the EEG using the intravenous anaesthetic drug propofol ⋮ A semi-infinite horizon LQ self-tuning regulator for ARMAX plants based on RLS ⋮ Adaptive predictive control with mean-square input constraint ⋮ Survey of industrial optimized adaptive control ⋮ Mixed Logical Dynamical Nonlinear Model Predictive Controller for Large‐Scale Solar Fields ⋮ Sidestepping the positive real condition in RELS via multiple RLS identifiers ⋮ Indirect and implicit adaptive predictive control of the benchmark plant

Cites Work

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• Uniqueness of local minima for linear quadratic control design
• Predictor-based self-tuning control
• Implicit and explicit LQG self-tuning controllers
• Adaptive control based on explicit criterion minimization
• Performance improvements of self-tuning controllers by multistep horizons: The MUSMAR approach
• Generalized predictive control. II: Extensions and interpretations
• On the absence of positive realness conditions in self-tuning regulators based on explicit criterion minimization
• Removal of a positive realness condition in minimum variance adaptive regulators by multistep horizons
• Analysis of recursive stochastic algorithms

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