Continuous-time constrained least-squares algorithms for recursive parameter estimation of stochastic linear systems by a stabilized output-error method

DOI10.1080/00207178608933675zbMath0604.93066OpenAlexW2142292677MaRDI QIDQ3743237

A. J. Udink Ten Cate

Publication date: 1986

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

Full work available at URL: http://pure.iiasa.ac.at/id/eprint/2647/1/WP-85-054.pdf

zbMATH Keywords

convergenceLyapunov functionleast-squaresrecursive algorithmscontinuous-timeoutput errorpositive realness condition

Mathematics Subject Classification ID

System identification (93B30) Linear systems in control theory (93C05) Identification in stochastic control theory (93E12)

Related Items (1)

A contribution on the parallel adaptive observer

Cites Work

• Unnamed Item
• Unnamed Item
• Unnamed Item
• Dynamic system identification. Experiment design and data analysis
• Recursive output error identification algorithms theory and evaluation
• Parameter estimation for continuous-time models - a survey
• Estimation of continuous-time linear system parameters from periodic data
• Exponential convergence of adaptive identification and control algorithms
• Stochastic approximation methods for constrained and unconstrained systems
• Some aspects of recursive parameter estimation
• Design of an adaptive observer and its application to an adaptive pole placement controller
• Unbiased recursive identification using model reference adaptive techniques
• Exponential stability of linear equations arising in adaptive identification
• Probing signals for model reference identification
• Analysis of recursive stochastic algorithms
• A least-squares like gradient method for discrete process identification

This page was built for publication: Continuous-time constrained least-squares algorithms for recursive parameter estimation of stochastic linear systems by a stabilized output-error method