An adaptive optimization scheme with satisfactory transient performance

Adaptive optimization (AO) schemes based on stochastic approximation principles such as the Random Directions Kiefer–Wolfowitz (RDKW), the Simultaneous Perturbation Stochastic Approximation (SPSA) and the Adaptive Fine-Tuning (AFT) algorithms possess the serious disadvantage of not guaranteeing sati...

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Bibliographic Details
Published in:Automatica (Oxford) 2009-03, Vol.45 (3), p.716-723
Main Author: Kosmatopoulos, Elias B.
Format: Article
Language:English
Subjects:
Adaptative systems
Adaptive fine-tuning
Adaptive optimization
Applied sciences
Computer science
control theory
systems
Control theory. Systems
Exact sciences and technology
Kiefer–Wolfowitz procedure
Simultaneous perturbation stochastic approximation (SPSA)
Stochastic approximation
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Summary:Adaptive optimization (AO) schemes based on stochastic approximation principles such as the Random Directions Kiefer–Wolfowitz (RDKW), the Simultaneous Perturbation Stochastic Approximation (SPSA) and the Adaptive Fine-Tuning (AFT) algorithms possess the serious disadvantage of not guaranteeing satisfactory transient behavior due to their requirement for using random or random-like perturbations of the parameter vector. The use of random or random-like perturbations may lead to particularly large values of the objective function, which may result to severe poor performance or stability problems when these methods are applied to closed-loop controller optimization applications. In this paper, we introduce and analyze a new algorithm for alleviating this problem. Mathematical analysis establishes satisfactory transient performance and convergence of the proposed scheme under a general set of assumptions. Application of the proposed scheme to the adaptive optimization of a large-scale, complex control system demonstrates the efficiency of the proposed scheme.
ISSN:0005-1098
1873-2836
DOI:10.1016/j.automatica.2008.09.014