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Functional adaptive controller for multivariable stochastic systems with dynamic structure of neural network
Author(s) -
Král L.,
Šimandl M.
Publication year - 2011
Publication title -
international journal of adaptive control and signal processing
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.73
H-Index - 66
eISSN - 1099-1115
pISSN - 0890-6327
DOI - 10.1002/acs.1250
Subject(s) - multivariable calculus , artificial neural network , control theory (sociology) , computer science , controller (irrigation) , dual (grammatical number) , perceptron , adaptive control , process (computing) , pruning , control (management) , mathematical optimization , control engineering , artificial intelligence , mathematics , engineering , art , agronomy , literature , biology , operating system
The article deals with a challenging problem of adaptive control design for multivariable stochastic systems with a functional uncertainty. Model of the system is based on multi‐layered perceptron neural networks where both the unknown parameters and the structure are found in real time without a necessity of any off‐line training process. The unknown parameters are estimated by a global estimation method, the Gaussian sum filter, and the structure of the neural network model is optimized by a proposed pruning method. The control law is based on a bicriterial approach to the suboptimal dual control. Two individual criteria are designed and used to introduce conflicting efforts between the estimation and control; probing and caution. A comparison of the proposed dual control and its alternative with an implementation of the pruning algorithm is shown in a numerical example. Copyright © 2011 John Wiley & Sons, Ltd.