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Optimal adaptive control systems for devices having delay

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Bibliographic Details
Title: Optimal adaptive control systems for devices having delay
Authors: Tsypkin, Ya. Z.
Publisher Information: Springer US, New York, NY; Pleiades Publishing, New York, NY; MAIK ``Nauka/Interperiodica'', Moscow
Subject Terms: Discrete-time control/observation systems, Computational methods in stochastic control, Adaptive control/observation systems, extended squared-error criterion, optimal predictor structures, Optimal stochastic control, adaptive discrete control algorithm, Existence of optimal solutions to problems involving randomness
Description: It is argued that convergence cannot characterize the quality of the adaptation algorithms and thus digital modelling plays a major role by allowing an adaptive control algorithm to be compared with various adaptation algorithms. Optimal adaptation algorithms have the highest possible rate of convergence which is valid to evaluate their quality. A general approach to solving the problem of synthesizing an adaptive discrete control algorithm is presented in the paper involving an extended squared-error criterion and optimal predictor structures. Optimal adaptation algorithms are established and their properties and differences are explained.
Document Type: Article
File Description: application/xml
Access URL: https://zbmath.org/4008212
Accession Number: edsair.c2b0b933574d..cbf6d1ef88b5a861a10726d0738eb25b
Database: OpenAIRE
Description
Abstract:It is argued that convergence cannot characterize the quality of the adaptation algorithms and thus digital modelling plays a major role by allowing an adaptive control algorithm to be compared with various adaptation algorithms. Optimal adaptation algorithms have the highest possible rate of convergence which is valid to evaluate their quality. A general approach to solving the problem of synthesizing an adaptive discrete control algorithm is presented in the paper involving an extended squared-error criterion and optimal predictor structures. Optimal adaptation algorithms are established and their properties and differences are explained.