Adaptive pole placement algorithms for of non-minimum-phase stochastic systems

  • № 5-6 2020

Страницы: 

38

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43

Язык: английский

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Аннотация

Adaptive pole placement algorithms for non-minimal phase stochastic systems are presented. The characterization of the structure and the set of models of linear dynamic control objects are carried out. As an example, the algorithm of the extended self-tuning controller is considered. To find the estimate of the controller parameters, the recursive least squares method is used. An explicit algorithm is proposed that makes it possible to exclude the operation of matrix inversion, which requires significant computational costs. Pole placement algorithms are given in the presence of white noise at the output. These algorithms belong to the class of stochastic approximation algorithms, the convergence of which is much lower than that of recurrent least squares algorithms. The results of numerical analysis have confirmed their efficiency, which makes it possible to use them in solving applied problems of identification and synthesis of adaptive control systems for technological objects.

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