Temat: self-tuning - Katalog OPAC zbiorów

Skocz do pozycji: 1.

Tytuł:: Self-Tuning Generalized Predictive Control With Input Constraints
Autorzy:: Królikowski, A.
Jerzy, D.
Powiązania:: https://bibliotekanauki.pl/articles/908330.pdf
Data publikacji:: 2001
Wydawca:: Uniwersytet Zielonogórski. Oficyna Wydawnicza
Tematy:: samostrojenie
sterowanie
generalized predictive control
constraints
self-tuning
ARIMAX/ARMAX systems
Opis:: The handling of various input constraints in the self-tuning generalized predictive control (STGPC) problem of ARIMAX/ARMAX systems is considered. The methods based on the Lagrange multipliers and Lemke's algorithm are used to solve the constrained optimization problem. A self-tuning controller is implemented in an indirect way, and the considered constraints imposed on the control input signal are of the rate, amplitude and energy types. A comparative simulation study of self-tuning control system behaviour is given with respect to the design parameters and constraints. The stability of a closed-loop control system is analyzed and the computational loads of both the methods are compared.
Źródło:: International Journal of Applied Mathematics and Computer Science; 2001, 11, 2; 459-479
1641-876X
2083-8492
Pojawia się w:: International Journal of Applied Mathematics and Computer Science
Dostawca treści:: Biblioteka Nauki

Artykuł

Skocz do pozycji: 2.

Tytuł:: Stochastic multivariable self-tuning tracker for non-Gaussian systems
Autorzy:: Filipovic, V.
Powiązania:: https://bibliotekanauki.pl/articles/908532.pdf
Data publikacji:: 2005
Wydawca:: Uniwersytet Zielonogórski. Oficyna Wydawnicza
Tematy:: model ARMAX
statystyka odpornościowa
stabilność globalna
ARMAX model
self-tuning tracker
non-Gaussian noise
robust statistics
global stability
optimality
Opis:: This paper considers the properties of a minimum variance self-tuning tracker for MIMO systems described by ARMAX models. It is assumed that the stochastic noise has a non-Gaussian distribution. Such an assumption introduces into a recursive algorithm a nonlinear transformation of the prediction error. The system under consideration is minimum phase with different dimensions for input and output vectors. In the paper the concept of Kronecker’s product is used, which allows us to represent unknown parameters in the form of vectors. For parameter estimation a stochastic approximation algorithm is employed. Using the concept of the stochastic Lyapunov function, global stability and optimality of the feedback system are established.
Źródło:: International Journal of Applied Mathematics and Computer Science; 2005, 15, 3; 351-357
1641-876X
2083-8492
Pojawia się w:: International Journal of Applied Mathematics and Computer Science
Dostawca treści:: Biblioteka Nauki

Artykuł

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