Self-tuning control based on multi-innovation stochastic gradient parameter estimation

DOI10.1016/j.sysconle.2008.08.005zbMath1154.93040OpenAlexW2074918810MaRDI QIDQ999835

Jiabo Zhang, Yang Shi, Feng Ding

Publication date: 10 February 2009

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

Full work available at URL: https://doi.org/10.1016/j.sysconle.2008.08.005

zbMATH Keywords

recursive identificationparameter estimationadaptive controlstochastic gradientself-tuning controlmulti-innovation identification methods

Mathematics Subject Classification ID

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Cites Work

• Performance analysis of multi-innovation gradient type identification methods
• Multi-innovation least squares identification methods based on the auxiliary model for MISO systems
• Strong consistency of parameter estimates in direct self-tuning control algorithms based on stochastic approximation
• On self tuning regulators
• Identification of Hammerstein nonlinear ARMAX systems
• Adaptive control with recursive identification for stochastic linear systems
• Stability and instability of limit points for stochastic approximation algorithms
• Stochastic approximation and user adaptation in a competitive resource sharing system
• Almost sure rate of convergence of the parameter estimates in stochastic approximation algorithm
• Least squares based self‐tuning control of dual‐rate systems
• General results on the convergence of stochastic algorithms
• Stochastic approximation in nonparametric identification of Hammerstein systems
• Adaptive Digital Control of Hammerstein Nonlinear Systems with Limited Output Sampling
• Unnamed Item

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