Identification and Adaptive Control of Change-Point ARX Models Via Rao-Blackwellized Particle Filters
Yuguo Chen
Tze Leung Lai
Dept. of Stat., Illinois Univ., Champaign, IL;
This paper appears in: Automatic Control, IEEE Transactions on
Publication Date: Jan. 2007
Volume: 52,
Issue: 1
On page(s): 67-72
ISSN: 0018-9286
INSPEC Accession Number: 9268782
Digital Object Identifier: 10.1109/TAC.2006.887913
Current Version Published: 2007-01-15
Abstract
By proper choice of proposal distributions for importance sampling and of resampling schemes for sequentially updating the importance weights, we address the problem of on-line identification and adaptive control of autoregressive models with exogenous inputs (ARX models) with Markov parameter jumps. Particle filters that can be implemented online via parallel recursions are developed by making use of explicit formulas of the posterior means of the time-varying parameters. Theoretical analysis and simulation studies show improvements of this approach over conventional methods
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