Temat: neural networks control - Katalog OPAC zbiorów

Skocz do pozycji: 1.

Tytuł:: Neural Network-Based Narx Models in Non-Linear Adaptive Control
Autorzy:: Dzieliński, A.
Powiązania:: https://bibliotekanauki.pl/articles/907986.pdf
Data publikacji:: 2002
Wydawca:: Uniwersytet Zielonogórski. Oficyna Wydawnicza
Tematy:: automatyka
neural networks
adaptive control
nonlinear systems
Opis:: The applicability of approximate NARX models of non-linear dynamic systems is discussed. The models are obtained by a new version of Fourier analysis-based neural network also described in the paper. This constitutes a reformulation of a known method in a recursive manner, i.e. adapted to account for incoming data on-line. The method allows us to obtain an approximate model of the non-linear system. The estimation of the influence of the modelling error on the discrepancy between the model and real system outputs is given. Possible applications of this approach to the design of BIBO stable closed-loop control are proposed.
Źródło:: International Journal of Applied Mathematics and Computer Science; 2002, 12, 2; 235-240
1641-876X
2083-8492
Pojawia się w:: International Journal of Applied Mathematics and Computer Science
Dostawca treści:: Biblioteka Nauki

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Skocz do pozycji: 2.

Tytuł:: Soft computing in model-based predictive control
Autorzy:: Tatjewski, P.
Ławryńczuk, M.
Powiązania:: https://bibliotekanauki.pl/articles/908473.pdf
Data publikacji:: 2006
Wydawca:: Uniwersytet Zielonogórski. Oficyna Wydawnicza
Tematy:: sterowanie procesami
sterowanie predykcyjne
system nieliniowy
system rozmyty
sieć neuronowa
process control
model predictive control
nonlinear systems
fuzzy systems
neural networks
Opis:: The application of fuzzy reasoning techniques and neural network structures to model-based predictive control (MPC) is studied. First, basic structures of MPC algorithms are reviewed. Then, applications of fuzzy systems of the Takagi-Sugeno type in explicit and numerical nonlinear MPC algorithms are presented. Next, many techniques using neural network modeling to improve structural or computational properties of MPC algorithms are presented and discussed, from a neural network model of a process in standard MPC structures to modeling parts or entire MPC controllers with neural networks. Finally, a simulation example and conclusions are given.
Źródło:: International Journal of Applied Mathematics and Computer Science; 2006, 16, 1; 7-26
1641-876X
2083-8492
Pojawia się w:: International Journal of Applied Mathematics and Computer Science
Dostawca treści:: Biblioteka Nauki

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Skocz do pozycji: 3.

Tytuł:: Stabilising solutions to a class of nonlinear optimal state tracking problems using radial basis function networks
Autorzy:: Ahmida, Z.
Charef, A.
Becerra, V. M.
Powiązania:: https://bibliotekanauki.pl/articles/908523.pdf
Data publikacji:: 2005
Wydawca:: Uniwersytet Zielonogórski. Oficyna Wydawnicza
Tematy:: system nieliniowy
sterowanie optymalne
radialna funkcja bazowa
sieć neuronowa
regulacja predykcyjna
sterowanie wyprzedzające
nonlinear systems
optimal control
radial basis functions
neural networks
predictive control
feedforward control
Opis:: A controller architecture for nonlinear systems described by Gaussian RBF neural networks is proposed. The controller is a stabilising solution to a class of nonlinear optimal state tracking problems and consists of a combination of a state feedback stabilising regulator and a feedforward neuro-controller. The state feedback stabilising regulator is computed online by transforming the tracking problem into a more manageable regulation one, which is solved within the framework of a nonlinear predictive control strategy with guaranteed stability. The feedforward neuro-controller has been designed using the concept of inverse mapping. The proposed control scheme is demonstrated on a simulated single-link robotic manipulator.
Źródło:: International Journal of Applied Mathematics and Computer Science; 2005, 15, 3; 369-381
1641-876X
2083-8492
Pojawia się w:: International Journal of Applied Mathematics and Computer Science
Dostawca treści:: Biblioteka Nauki

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Skocz do pozycji: 4.

Tytuł:: Neural network-based MRAC control of dynamic nonlinear systems
Autorzy:: Debbache, G.
Bennia, A.
Goléa, N.
Powiązania:: https://bibliotekanauki.pl/articles/908401.pdf
Data publikacji:: 2006
Wydawca:: Uniwersytet Zielonogórski. Oficyna Wydawnicza
Tematy:: sieć neuronowa
model odniesienia
system nieliniowy
sterowanie adaptacyjne
obserwator
stabilność
neural networks
reference model
nonlinear systems
adaptive control
observer
stability
Opis:: This paper presents direct model reference adaptive control for a class of nonlinear systems with unknown nonlinearities. The model following conditions are assured by using adaptive neural networks as the nonlinear state feedback controller. Both full state information and observer-based schemes are investigated. All the signals in the closed loop are guaranteed to be bounded and the system state is proven to converge to a small neighborhood of the reference model state. It is also shown that stability conditions can be formulated as linear matrix inequalities (LMI) that can be solved using efficient software algorithms. The control performance of the closed-loop system is guaranteed by suitably choosing the design parameters. Simulation results are presented to show the effectiveness of the approach.
Źródło:: International Journal of Applied Mathematics and Computer Science; 2006, 16, 2; 219-232
1641-876X
2083-8492
Pojawia się w:: International Journal of Applied Mathematics and Computer Science
Dostawca treści:: Biblioteka Nauki

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