Temat: fuzzy PID control - Katalog OPAC zbiorów

Skocz do pozycji: 1.

Tytuł:: Simulation and implementation of alternative attitude control algorithms for a micro multirotor flying platform
Autorzy:: Gąsior, P.
Gąsior, M.
Powiązania:: https://bibliotekanauki.pl/articles/114654.pdf
Data publikacji:: 2015
Wydawca:: Stowarzyszenie Inżynierów i Techników Mechaników Polskich
Tematy:: quadrotor
control
fuzzy logic
ANN
PID
NeuroQuad
Opis:: The most popular control algorithm in attitude stabilization of multirotor aerial vehicles is a PID controller. It is used so frequently because of low computational requirements and simplicity of implementation and tuning. However, there are many other control algorithms suitable for this task, for example fuzzy logic or neural networks controllers. These algorithms are more complex than PID, but with appropriate knowledge and hardware it is possible to implement them in the on-board controller. This paper presents the comparison of the mentioned algorithms during simulation experiments. The construction process of a micro multirotor flying platform NeuroQuad and the implementation of the tested algorithms on this platform are described.
Źródło:: Measurement Automation Monitoring; 2015, 61, 7; 346-348
2450-2855
Pojawia się w:: Measurement Automation Monitoring
Dostawca treści:: Biblioteka Nauki

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Skocz do pozycji: 2.

Tytuł:: Comparison of energy consumption in the classical (PID) and fuzzy control of foundry resistance furnace
Autorzy:: Ziółkowski, E.
Śmierciak, P.
Powiązania:: https://bibliotekanauki.pl/articles/380361.pdf
Data publikacji:: 2012
Wydawca:: Polska Akademia Nauk. Czytelnia Czasopism PAN
Tematy:: automatyzacja procesów odlewniczych
piec odlewniczy
regulator PID
sterownik rozmyty
automation of foundry processes
control of foundry furnaces
PID
fuzzy controllers
Opis:: Foundry resistance furnaces are thermal devices with a relatively large time delay in their response to a change in power parameters. Commonly used in automation classical PID controllers do not meet the requirements of high-quality control. Developed in recent years, fuzzy control theory is increasingly being used in various branches of economy and industry. Fuzzy controllers allow to introduce new developments in control systems of foundry furnaces as well. Correctly selected fuzzy controller can significantly reduce energy consumption in a controlled thermal process of heating equipment. The article presents a comparison of energy consumption by control system of foundry resistance furnace, equipped with either a PID controller or fuzzy controller optimally chosen.
Źródło:: Archives of Foundry Engineering; 2012, 12, 3; 129-132
1897-3310
2299-2944
Pojawia się w:: Archives of Foundry Engineering
Dostawca treści:: Biblioteka Nauki

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Skocz do pozycji: 3.

Tytuł:: Simulation and Analysis of Sintering Furnace Temperature Based on Fuzzy Neural Network Control
Autorzy:: Chaoxin, Zou
Rong, Li
Zhiping, Xie
Ming, Su
Jingshi, Zeng
Xu, Ji
Xiaoli, Ye
Ye, Wang
Powiązania:: https://bibliotekanauki.pl/articles/1837849.pdf
Data publikacji:: 2021
Wydawca:: Polska Akademia Nauk. Czytelnia Czasopism PAN
Tematy:: fuzzy neural network
furnace
sintering
temperature control
PID
sieć neuronowa rozmyta
piec
spiekanie
kontrola temperatury
Opis:: Aiming at the problems of delay and couple in the sintering temperature control system of lithium batteries, a fuzzy neural network controller that can solve complex nonlinear temperature control is designed in this paper. The influence of heating voltage, air inlet speed and air inlet volume on the control of temperature of lithium battery sintering is analyzed, and a fuzzy control system by using MATLAB toolbox is established. And on this basis, a fuzzy neural network controller is designed, and then a PID control system and a fuzzy neural network control system are established through SIMULINK. The simulation shows that the response time of the fuzzy neural network control system compared with the PID control system is shortened by 24s, the system stability adjustment time is shortened by 160s, and the maximum overshoot is reduced by 6.1%. The research results show that the fuzzy neural network control system can not only realize the adjustment of lithium battery sintering temperature control faster, but also has strong adaptability, fault tolerance and anti-interference ability.
Źródło:: Archives of Foundry Engineering; 2021, 21, 1; 23-30
1897-3310
2299-2944
Pojawia się w:: Archives of Foundry Engineering
Dostawca treści:: Biblioteka Nauki

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Skocz do pozycji: 4.

Tytuł:: Simulation and Analysis of Sintering Furnace Temperature Based on Fuzzy Neural Network Control
Autorzy:: Chaoxin, Zou
Rong, Li
Zhiping, Xie
Ming, Su
Jingshi, Zeng
Xu, Ji
Xiaoli, Ye
Ye, Wang
Powiązania:: https://bibliotekanauki.pl/articles/1837792.pdf
Data publikacji:: 2021
Wydawca:: Polska Akademia Nauk. Czytelnia Czasopism PAN
Tematy:: fuzzy neural network
furnace
sintering
temperature control
PID
sieć neuronowa rozmyta
piec
spiekanie
kontrola temperatury
Opis:: Aiming at the problems of delay and couple in the sintering temperature control system of lithium batteries, a fuzzy neural network controller that can solve complex nonlinear temperature control is designed in this paper. The influence of heating voltage, air inlet speed and air inlet volume on the control of temperature of lithium battery sintering is analyzed, and a fuzzy control system by using MATLAB toolbox is established. And on this basis, a fuzzy neural network controller is designed, and then a PID control system and a fuzzy neural network control system are established through SIMULINK. The simulation shows that the response time of the fuzzy neural network control system compared with the PID control system is shortened by 24s, the system stability adjustment time is shortened by 160s, and the maximum overshoot is reduced by 6.1%. The research results show that the fuzzy neural network control system can not only realize the adjustment of lithium battery sintering temperature control faster, but also has strong adaptability, fault tolerance and anti-interference ability.
Źródło:: Archives of Foundry Engineering; 2021, 21, 1; 23-30
1897-3310
2299-2944
Pojawia się w:: Archives of Foundry Engineering
Dostawca treści:: Biblioteka Nauki

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Skocz do pozycji: 5.

Tytuł:: Hierarchiczny system sterowania procesem mieszania. Implementacja sprzętowa i badania testowe
Hierarchical control system of mixing process. Hardware implementation and testing
Autorzy:: Błaszkiewicz, K.
Biniecki, P.
Piotrowski, R.
Powiązania:: https://bibliotekanauki.pl/articles/277283.pdf
Data publikacji:: 2012
Wydawca:: Sieć Badawcza Łukasiewicz - Przemysłowy Instytut Automatyki i Pomiarów
Tematy:: modelowanie matematyczne
proces mieszania
inżynieria chemiczna
hierarchiczny system sterowania
regulator rozmyty
PID
implementacja sprzętowa
mathematical modeling
mixing process
chemical engineering
hierarchical control system
fuzzy controller
hardware implementation
Opis:: Proces mieszania często występuje w różnych gałęziach przemysłu. W artykule rozważane jest sporządzanie mieszaniny aceton-octan etylu. Podstawowym celem jest uzyskanie właściwego stopnia jednorodności mieszaniny. W artykule zbudowano model matematyczny procesu mieszania i zaprojektowano warstwowy system sterowania. Składa się on z nadrzędnego regulatora rozmytego oraz warstwy bezpośredniej z regulatorami typu PID i sterowaniem binarnym. Przeprowadzono badania testowe działania układu sterowania w strukturze sprzętowej i dokonano analizy uzyskanych wyników sterowania.
Mixing process is very often used in many various fields of industry. In this paper the mixture of acetone and ethyl acetate is considered. Desire is to achieve proper homogeneity of mixture. The paper presents mathematical model of mixing process and designed two-layer structure of control system. The fuzzy controller is implemented in upper layer. Direct layer consists of PID controllers and binary control. Control system is tested by simulation for hardware in the loop control system. The control results are analyzed.
Źródło:: Pomiary Automatyka Robotyka; 2012, 16, 7-8; 90-96
1427-9126
Pojawia się w:: Pomiary Automatyka Robotyka
Dostawca treści:: Biblioteka Nauki

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