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    Wyświetlanie 1-8 z 8
    Tytuł:
    Multi motor neural PID relative coupling speed synchronous control
    Autorzy:
    Zhang, Yonglong
    An, Yuejun
    Wang, Guangyu
    Kong, Xiangling
    Powiązania:
    https://bibliotekanauki.pl/articles/141259.pdf
    Data publikacji:
    2020
    Wydawca:
    Polska Akademia Nauk. Czytelnia Czasopism PAN
    Tematy:
    fuzzy control
    multi motor synchronous control
    neural PID
    relative coupling control
    vacuum pump
    Opis:
    When the traditional multi-motor speed synchronous control strategy is applied to the vacuum pump system, it is prone to the drawbacks of large synchronization error. In this paper, a simplified mathematical model of the motor for a vacuum pump is established and the transfer function is introduced, which weakens the multivariable, strong coupling and nonlinear characteristics of the motor system. According to the basic principle of the relative coupling control strategy, the neural network Proportion Integration Differentiation (PID) is introduced as a speed compensator in this system. It effectively improves the synchronization and anti-interference ability of the multi motor
    Źródło:
    Archives of Electrical Engineering; 2020, 69, 1; 69-88
    1427-4221
    2300-2506
    Pojawia się w:
    Archives of Electrical Engineering
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Neural network-PID controller for roll fin stabilizer
    Autorzy:
    Ghassemi, H.
    Dadmarzi, F. H.
    Ghadimi, P.
    Ommani, B.
    Powiązania:
    https://bibliotekanauki.pl/articles/259283.pdf
    Data publikacji:
    2010
    Wydawca:
    Politechnika Gdańska. Wydział Inżynierii Mechanicznej i Okrętownictwa
    Tematy:
    Fin stabilizer
    neural network
    PID control
    restoring force
    Opis:
    Fin stabilizers are very effective devices for controlling the ship roll motion against external wave-generated moments. Lift forces due to flow around fin with an angle of attack produce anti - roll moment. Therefore control of attack angle plays important role in reducing roll of ships. This paper presents results of using a combined neural network and PID for roll control of ship with small draught. Numerical results are given of around-fin flow analysis with considering free surface effect modelled by neural network and imposed to controlling loop. Hydraulic machinery constraints are also considered in the modelling. The obtained results show good performance of the controller in reducing roll amplitude in random seas. The approach can be used for any irregular sea conditions.
    Źródło:
    Polish Maritime Research; 2010, 2; 23-28
    1233-2585
    Pojawia się w:
    Polish Maritime Research
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Identification of water treatment plant based on feedforward neural network
    Autorzy:
    Mohamed, A. F.
    Radwa, H. Z.
    Powiązania:
    https://bibliotekanauki.pl/articles/971051.pdf
    Data publikacji:
    2017
    Wydawca:
    Polska Akademia Nauk. Instytut Badań Systemowych PAN
    Tematy:
    water treatment plant
    coagulation process
    PID
    neural network control
    Opis:
    Coagulation process is the main process in conventional water treatment process sequence. It influences the following treatment process aspects: maintaining plant efficiency and increasing the quality of the produced water. This is accomplished by adding chemicals to raw water, such as alum sulphate. To secure the appropriate plant performance, a mathematical model is proposed in this paper for the coagulation unit, followed by the development of the control strategy. Classic PID and neural network based controller regulating the process are used. Tests were performed, based on the real data for water treatment, using MATLAB/SIMULINK. Simulation results showed better values for both settling time and overshoot in the case of using neural network based controller than PID.
    Źródło:
    Control and Cybernetics; 2017, 46, 3; 247-258
    0324-8569
    Pojawia się w:
    Control and Cybernetics
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Neurocontrolled car speed system
    Autorzy:
    Nakonechnyi, Markiyan
    Ivakhiv, Orest
    Świsulski, Dariusz
    Powiązania:
    https://bibliotekanauki.pl/articles/27314203.pdf
    Data publikacji:
    2022
    Wydawca:
    Sieć Badawcza Łukasiewicz - Przemysłowy Instytut Automatyki i Pomiarów
    Tematy:
    neural controller
    PID-algorithm of control
    dynamic object
    neural networks
    electric car
    speed control
    Opis:
    The features of the synthesis of neural controllers for the car speed control system are considered in this article. The task of synthesis is to determine the weight coefficients of neural networks that provide the implementation of proportional and proportional-integralderivative control laws. The synthesis of controllers is based on an approach that uses a reversed model of the standard. A model of the car speed control system with the use of permitting subsystems has been developed, with the help of the synthesized controller that is connected under certain specified conditions. With the iterative programming and mathematical modeling environment in MATLAB, and using the Simulink package, a structural scheme for controlling the speed of the car was constructed and simulated using synthesized neural controllers.
    Źródło:
    Journal of Automation Mobile Robotics and Intelligent Systems; 2022, 16, 3; 13--21
    1897-8649
    2080-2145
    Pojawia się w:
    Journal of Automation Mobile Robotics and Intelligent Systems
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Neuronowy regulator prędkości obrotowej odporny na zmiany bezwładności
    A neural speed controller robust to inertia changes
    Autorzy:
    Jakubowski, M.
    Nowakowski, K.
    Zawirski, K.
    Powiązania:
    https://bibliotekanauki.pl/articles/157600.pdf
    Data publikacji:
    2014
    Wydawca:
    Stowarzyszenie Inżynierów i Techników Mechaników Polskich
    Tematy:
    regulator neuronowy
    sterowanie neuronowe
    sztuczna inteligencja
    uczenie maszynowe
    PID
    neural controller
    neural controlling
    artificial intelligence
    machine learning
    Opis:
    W ramach niniejszej pracy zaprezentowany został neuronowy regulator prędkości obrotowej odporny na zmiany bezwładności. Celem pracy było opracowanie struktury regulatora oraz dobór optymalnego algorytmu uczenia. Stworzony regulator sterował pracą silnika prądu stałego. Metodologia prowadzonych badań zakładała zbadanie działania układu w szerokim zakresie zmian momentu obciążenia oraz bezwładności. Projektowanie przeprowadzono w taki sposób, aby badany układ napędowy wykazywały dobre właściwości regulacyjne w szerokim zakresie zmiany bezwładności obciążenia. Proces syntezy regulatora został szczegółowo opisany w ramach niniejszej pracy. Analizie poddano szereg badań symulacyjnych, w ramach których rozpatrywano wybrane wskaźniki jakości dla różnych wartości bezwładności oraz momentu obciążenia. Dokonano także analizy porównawczej badanego regulatora neuronowego z optymalnie nastrojonym klasycznym regulatorem PID. Uzyskane wyniki symulacyjne zostały przeniesione na grunt implementacji fizycznego obiektu sterowania.
    This paper presents a neural network speed controller that is robust to inertia changes. The main object of this study was to establish the structure of the controller and to create an optimal learning algorithm. Within the project, the created controller steered the operation of a DC motor. The methodology of the research involved studying the effects of the system over a wide range of load torque and inertia changes. The project was carried out in a such way that good regulatory properties over a wide range of inertia changes were performed for the drive systems. The synthesis of the controller is described in details in this paper. The analysis of series simulation studies including selected quality indicators for different values of inertia and different load torque is conducted. Moreover, the comparative analysis of the neural control and the optimally tuned classical PID controller is performed. The obtained simulation results were used for implementation of a physical control object.
    Źródło:
    Pomiary Automatyka Kontrola; 2014, R. 60, nr 10, 10; 840-844
    0032-4140
    Pojawia się w:
    Pomiary Automatyka Kontrola
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    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
    Artykuł
    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
    Artykuł
    Tytuł:
    Neural network based feedback linearization control of a servo-hydraulic vehicle suspension system
    Autorzy:
    Pedro, J. O.
    Dahunsi, O. A.
    Powiązania:
    https://bibliotekanauki.pl/articles/907825.pdf
    Data publikacji:
    2011
    Wydawca:
    Uniwersytet Zielonogórski. Oficyna Wydawnicza
    Tematy:
    sieć neuronowa
    sterowanie bezpośrednie
    sterowanie ze sprzężeniem zwrotnym
    regulacja PID
    komfort jazdy
    układ zawieszenia
    neural networks
    direct adaptive control
    feedback linearization control
    PID control
    ride comfort
    suspension system
    servo-hydraulics
    Opis:
    This paper presents the design of a neural network based feedback linearization (NNFBL) controller for a two degree-offreedom (DOF), quarter-car, servo-hydraulic vehicle suspension system. The main objective of the direct adaptive NNFBL controller is to improve the system's ride comfort and handling quality. A feedforward, multi-layer perceptron (MLP) neural network (NN) model that is well suited for control by discrete input-output linearization (NNIOL) is developed using input-output data sets obtained from mathematical model simulation. The NN model is trained using the Levenberg- Marquardt optimization algorithm. The proposed controller is compared with a constant-gain PID controller (based on the Ziegler-Nichols tuning method) during suspension travel setpoint tracking in the presence of deterministic road disturbance. Simulation results demonstrate the superior performance of the proposed direct adaptive NNFBL controller over the generic PID controller in rejecting the deterministic road disturbance. This superior performance is achieved at a much lower control cost within the stipulated constraints.
    Źródło:
    International Journal of Applied Mathematics and Computer Science; 2011, 21, 1; 137-147
    1641-876X
    2083-8492
    Pojawia się w:
    International Journal of Applied Mathematics and Computer Science
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
      Wyświetlanie 1-8 z 8

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