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Wyświetlanie 1-15 z 117
Tytuł:
Comparison of the adaptive and neural network control for LWR 4+ manipulators: simulation study
Autorzy:
Woliński, Łukasz
Powiązania:
https://bibliotekanauki.pl/articles/140152.pdf
Data publikacji:
2020
Wydawca:
Polska Akademia Nauk. Czytelnia Czasopism PAN
Tematy:
adaptive control
neural network control
redundant manipulator
Opis:
This paper deals with two control algorithms which utilize learning of their models’ parameters. An adaptive and artificial neural network control techniques are described and compared. Both control algorithms are implemented in MATLAB and Simulink environment, and they are used in the simulation of a postion control of the LWR 4+ manipulator subjected to unknown disturbances. The results, showing the better performance of the artificial neural network controller, are shown. Advantages and disadvantages of both controllers are discussed. The usefulness of the learning algorithms for the control of LWR 4+ robots is discussed. Preliminary experiments dealing with dynamic properties of the two LWR 4+ robots are reported.
Źródło:
Archive of Mechanical Engineering; 2020, LXVII, 1; 111-121
0004-0738
Pojawia się w:
Archive of Mechanical Engineering
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Neural model of the vehicle control system in a racing game. Part 2, Research experiments
Autorzy:
Bolesta, Arkadiusz
Tchórzewski, Jerzy
Powiązania:
https://bibliotekanauki.pl/articles/2175161.pdf
Data publikacji:
2022
Wydawca:
Uniwersytet Przyrodniczo-Humanistyczny w Siedlcach
Tematy:
Godot Engine
MATLAB
Simulink environment
Neural control system
Perceptron Artificial Neural Networks
video games
Opis:
This article, which is a continuation of the article under the same main title and subtitle: part 1 Design and its implementation, includes the obtained results of research experiments with the use of a designed and implemented racing game. It uses a neural model of the vehicle motion control system on the racetrack in the form of a Perceptron Artificial Neural Network (ANN). In designing the movement of vehicles on the racetrack, the following were used, inter alia, Godot Engine and MATLAB and Simulink programming environment. The numerical data (14 input quantities and two output quantities) for ANN training were prepared with the use of semi-automatic measurement of the race track control points. This article shows, among others, the results of 10 selected research experiments, testing and simulation, confirming the correct functioning of both the computer game and the model of the neural control system. As a result of simulation tests, it turned out that the longest lap of the track in the conducted experiments lasted 4 minutes and 55 seconds, and the shortest - 10.47 seconds. In five minutes, the highest number of laps was 34, while the lowest numbers of laps were 1 and 5. In the course of the experiments it was noticed that under the same conditions the ANN learning outcomes are sometimes different.
Źródło:
Studia Informatica : systems and information technology; 2022, 1(26); 45--60
1731-2264
Pojawia się w:
Studia Informatica : systems and information technology
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Neural network control design considerations for the active damping of a smart beam
Autorzy:
Cupiał, P.
Łacny, Ł.
Powiązania:
https://bibliotekanauki.pl/articles/280299.pdf
Data publikacji:
2015
Wydawca:
Polskie Towarzystwo Mechaniki Teoretycznej i Stosowanej
Tematy:
neural network control
smart structures
vibration damping
Opis:
In this study, possible options for the active damping of a smart beam with piezoelectric patches using neural network control algorithm, are presented. The algorithms used for the control are Neural Direct Inverse and Feedback Linearisation (NARMA-L2). Additionally, several possible modifications used for the purpose of improving the control, such as different values of control gain or sampling time of the training data, as well as step-wise control are tested.
Źródło:
Journal of Theoretical and Applied Mechanics; 2015, 53, 4; 767-774
1429-2955
Pojawia się w:
Journal of Theoretical and Applied Mechanics
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Combined system for off-line optimization and adaptive cutting force control
Autorzy:
Cus, F.
Balic, J.
Powiązania:
https://bibliotekanauki.pl/articles/100078.pdf
Data publikacji:
2010
Wydawca:
Wrocławska Rada Federacji Stowarzyszeń Naukowo-Technicznych
Tematy:
manufacturing processes
adaptive cutting force control
milling simulator
neural control strateg
off-line optimization
Opis:
The choice of manufacturing processes is based on cost, time and precision. A remaining drawback of modern CNC systems is that the machining parameters, such as feed-rate, cutting speed and depth of cut, are still programmed off-line. The machining parameters are usually selected before machining accordin to programmer's experience and machining handbooks. To prevent damage and to avoid machining failure the operating conditions are usually set extremely conservative. As a result, many CNC systems are inefficient and run under the operating conditions that are far from optimal . Even if the machining parameters are optimised off-line by an optimisation algorithm they cannot be adjusted during the machining process. In this paper, a neural adaptiv controller is developed and some simulations and experiments with the neural control strategy are carried out. The results demonstrate the ability of the proposed system to effectively regulate peak forces for cutting conditions commonly encountered in end milling operations.
Źródło:
Journal of Machine Engineering; 2010, 10, 2; 25-35
1895-7595
2391-8071
Pojawia się w:
Journal of Machine Engineering
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Intelligent control algorithm for ship dynamic positioning
Autorzy:
Meng, W.
Sheng, L. H.
Qing, M.
Rong, B. G.
Powiązania:
https://bibliotekanauki.pl/articles/229337.pdf
Data publikacji:
2014
Wydawca:
Polska Akademia Nauk. Czytelnia Czasopism PAN
Tematy:
dynamic positioning
fuzzy control
neural network control
BP algorithm
Opis:
Ship motion in the sea is a complex nonlinear kinematics. The hydrodynamic coefficients of ship model are very difficult to accurately determine. Establishing accurate mathematical model of ship motion is difficult because of changing random factors in the marine environment. Aiming at seeking a method of control to realize ship positioning, intelligent control algorithms are adopt utilizing operator's experience. Fuzzy controller and the neural network controller are respectively designed. Through simulations and experiments, intelligent control algorithm can deal with the complex nonlinear motion, and has good robustness. The ship dynamic positioning system with neural network control has high positioning accuracy and performance.
Źródło:
Archives of Control Sciences; 2014, 24, 4; 479-497
1230-2384
Pojawia się w:
Archives of Control Sciences
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Design of a multivariable neural controller for control of a nonlinear MIMO plant
Autorzy:
Bańka, S.
Dworak, P.
Jaroszewski, K.
Powiązania:
https://bibliotekanauki.pl/articles/330790.pdf
Data publikacji:
2014
Wydawca:
Uniwersytet Zielonogórski. Oficyna Wydawnicza
Tematy:
MIMO multivariable control system
nonlinear system
neural control
wielowymiarowy układ sterowania
układ nieliniowy
sterowanie neuronowe
Opis:
The paper presents the training problem of a set of neural nets to obtain a (gain-scheduling, adaptive) multivariable neural controller for control of a nonlinear MIMO dynamic process represented by a mathematical model of Low-Frequency (LF) motions of a drillship over the drilling point at the sea bottom. The designed neural controller contains a set of neural nets that determine values of its parameters chosen on the basis of two measured auxiliary signals. These are the ship’s current forward speed measured with respect to water and the systematically calculated difference between the course angle and the sea current (yaw angle). Four different methods for synthesis of multivariable modal controllers are used to obtain source data for training the neural controller with parameters reproduced by neural networks. Neural networks are designed on the basis of 3650 modal controllers obtained with the use of the pole placement technique after having linearized the model of LF motions made by the vessel at its nominal operating points in steady states that are dependent on the specified yaw angle and the sea current velocity. The final part of the paper includes simulation results of system operation with a neural controller along with conclusions and final remarks.
Źródło:
International Journal of Applied Mathematics and Computer Science; 2014, 24, 2; 357-369
1641-876X
2083-8492
Pojawia się w:
International Journal of Applied Mathematics and Computer Science
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Sterowanie neuronowe robotem równoległym - projekt i implementacja
Neural control of a parallel robot - design and implementation
Autorzy:
Petko, M.
Powiązania:
https://bibliotekanauki.pl/articles/156675.pdf
Data publikacji:
2006
Wydawca:
Stowarzyszenie Inżynierów i Techników Mechaników Polskich
Tematy:
implementacja
projektowanie
sterowanie neuronowe robotem równoległym
neural control of parallel robot
implementation
project
Opis:
W artykule przedstawiono projekt i implementację sterownika neuronowe-go równoległego robota o trzech stopniach swobody, przeznaczonego do frezowania. Sterownik jest oparty na neuronowym modelu odwrotnej dynamiki manipulatora uczonego na danych zebranych przy zastosowaniu stabilizującego sterownika wykorzystującego strukturalny model anali-tyczny manipulatora. Po zrealizowaniu wirtualnego i szybkiego prototy-powania sterownik został zaimplementowany w układzie FPGA z wpro-gramowanym mikroprocesorem. Współbieżna implementacja sprzętowo-programowa umożliwiła badanie możliwych realizacji algorytmu.
The paper presents design and implementation of neural controller for 3-DOF parallel robot for milling. The controller is based on neural model of the inverse dynamics of the manipulator, trained on data collected with the use of a computed torque stabilizing controller. After successful virtual and fast prototyping, the controller was implemented in a FPGA with a soft-processor. Hardware-Software Co-design allowed for exploration of possible control algorithm realisations.
Źródło:
Pomiary Automatyka Kontrola; 2006, R. 52, nr 5, 5; 31-34
0032-4140
Pojawia się w:
Pomiary Automatyka Kontrola
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Recurrent neural identification and control of a continuous bioprocess via first and second order learning
Autorzy:
Baruch, I.
Mariaca-Gaspar, C. R.
Powiązania:
https://bibliotekanauki.pl/articles/385133.pdf
Data publikacji:
2010
Wydawca:
Sieć Badawcza Łukasiewicz - Przemysłowy Instytut Automatyki i Pomiarów
Tematy:
backpropagation learning
direct adaptive neural control
indirect adaptive sliding mode control
Kalman filter recurrent neural network identifier
Levenberg-Marquardt learning
Opis:
This paper applies a new Kalman Filter Recurrent Neural Network (KFRNN) topology and a recursive Levenberg-Mar quardt (L-M) learning algorithm capable to estimate para meters and states of highly nonlinear unknown plant in noisy environment. The proposed KFRNN identifier, learned by the Backpropagation and L-M learning algorithm, was incorporated in a direct and indirect adaptive neural con trol schemes. The proposed control schemes were applied for real-time recurrent neural identification and control of a continuous stirred tank bioreactor model, where fast convergence, noise filtering and low mean squared error of reference tracking were achieved.
Źródło:
Journal of Automation Mobile Robotics and Intelligent Systems; 2010, 4, 4; 37-52
1897-8649
2080-2145
Pojawia się w:
Journal of Automation Mobile Robotics and Intelligent Systems
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:
Dedicated neural network design for friction compensation in robot drives
Projektowanie struktury sieci neuronowej dla celów eliminacji tarcia w napędach robotów
Autorzy:
Korendo, Z.
Uhl, T.
Powiązania:
https://bibliotekanauki.pl/articles/281390.pdf
Data publikacji:
2002
Wydawca:
Polskie Towarzystwo Mechaniki Teoretycznej i Stosowanej
Tematy:
friction modelling
mechatronics
neural networks for control
Opis:
In the paper we demonstrate a neural network-based controller design and prototyping following the mechatronic approach. A unified treatment of all system components (mechanical, eletrical and computational) is made possible thanks to the integrated software-hardware platform. The neural network in the presented approach is used to privide a linearising feedback loop for friction compensation in a robot drive. The efficiency of the experimental friction identification is improved thanks to dedicated network architecture. The proposed solution is implemented in DSP hardware and the simulation results are verified through laboratory experiments.
W pracy przedstawiono oparty na sieciach neuronowych układ sterowania napędem robota. Przedstawiono proces projektowania i prototypowania oparty na podejściu mechatronicznym. Sieć neuronowa w proponowanym rozwiązaniu spełnia rolę lineryzującej pętli sprzężenia zwrotnego. Jej podstawowym zadaniem jest kompensacja wpływu tarcia w napędzie robota. Zaproponowano specjalizowaną architekturę sieci neuronowej dostosowaną do modelowania tarcia. Uczenie sieci odbywa się na podstawie danych eksperymentalnych. Zaproponowaną sieć neuronową zaimplementowano z zastosowaniem techniki szybkiego prototypowania z wykorzystaniem procesorów sygnałowych. Wyniki symulacji porównano z wynikami eksperymentu na rzeczywistym obiekcie. Przedstawione podejście, jak wykazały uzyskane rezultaty, daje dobre wyniki w zakresie linearyzacji układów sterowania robotami z uwzględnieniem tarcia.
Źródło:
Journal of Theoretical and Applied Mechanics; 2002, 40, 3; 595-610
1429-2955
Pojawia się w:
Journal of Theoretical and Applied Mechanics
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Dynamic external force feedback loop control of a robot manipulator using a neural compensator - Application to the trajectory following in an unknown environment
Autorzy:
Ferguene, F.
Toumi, R.
Powiązania:
https://bibliotekanauki.pl/articles/907867.pdf
Data publikacji:
2009
Wydawca:
Uniwersytet Zielonogórski. Oficyna Wydawnicza
Tematy:
sterowanie położeniem
sterowanie siłowe
struktura zewnętrzna
sterowanie neuronowe
manipulator robota
force/position control
external structure
neural control
robot manipulator
Opis:
Force/position control strategies provide an effective framework to deal with tasks involving interaction with the environment. One of these strategies proposed in the literature is external force feedback loop control. It fully employs the available sensor measurements by operating the control action in a full dimensional space without using selection matrices. The performance of this control strategy is affected by uncertainties in both the robot dynamic model and environment stiffness. The purpose of this paper is to improve controller robustness by applying a neural network technique in order to compensate the effect of uncertainties in the robot model. We show that this control strategy is robust with respect to payload uncertainties, position and environment stiffness, and dry and viscous friction. Simulation results for a three degrees-of-freedom manipulator and various types of environments and trajectories show the effectiveness of the suggested approach compared with classical external force feedback loop structures.
Źródło:
International Journal of Applied Mathematics and Computer Science; 2009, 19, 1; 113-126
1641-876X
2083-8492
Pojawia się w:
International Journal of Applied Mathematics and Computer Science
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
A stability based neural networks controller design method
Autorzy:
Song, J.
Xu, X.
He, X.
Powiązania:
https://bibliotekanauki.pl/articles/206120.pdf
Data publikacji:
1998
Wydawca:
Polska Akademia Nauk. Instytut Badań Systemowych PAN
Tematy:
sieć neuronowa
stabilność
sterowanie nieliniowe
neural networks control
nonlinear control
sliding mode
stability
Opis:
The use of neural networks in control systems can be seen as a natural step in the evolution of control methodology to meet new challenges. Many attempts have been made to apply the neural networks to deal with non-linearities and uncertainties of the control systems. Research in neural network applications to control can be classified according to the major methods depending on structures of the control system, such as NN-based NON-linear System Identification, NN-based Supervised Control, NN-based Direct Control, NN-based Indirect Control, NN-based Adaptive Control, NN-based Self-learning Control, NN-based Fuzzy Control, and NN Variable Structure Control. All these control methods cannot, however, effectively guarantee system stability, i.e. none of these neural network controls, except for NN-based Variable Structure Control, is based on system stability. This also limits the application and development of the neural networks in control theory. The paper shows the effort to solve this difficulty and give a way for the design method of the stability based neural networks controller using Lyapunov second stability theorem. This kind of controller can not only guarantee system stability, but also fully compensate for the influence of system uncertainties and non-linearities.Simulation results also show the effectiveness of the controller.
Źródło:
Control and Cybernetics; 1998, 27, 1; 119-133
0324-8569
Pojawia się w:
Control and Cybernetics
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Zastosowanie regulatorów neuronowego i rozmytego do sterowania poziomem wody w układzie kaskadowym dwóch zbiorników
Use of neural and fuzzy controllers to control water level in two-tank cascade system
Autorzy:
Tomera, M.
Kasprowicz, A.
Powiązania:
https://bibliotekanauki.pl/articles/223272.pdf
Data publikacji:
2012
Wydawca:
Akademia Marynarki Wojennej. Wydział Dowodzenia i Operacji Morskich
Tematy:
sterowanie rozmyte
sterowanie neuronowe
radialne funkcje bazowe
układ kaskadowy dwóch zbiorników
mikrokontroler sygnałowy
fuzzy control
neural control
radial base functions
two-tank cascade system
digital signal processor
Opis:
W artykule przedstawione zostały regulatory zbudowane w oparciu o metody sztucznej inteligencji. Klasyczny regulator PID zastosowany do sterowania poziomem wody w układzie kaskadowym dwóch zbiorników zastąpiony został regulatorami rozmytym i neuronowym. Struktura regulatora rozmytego działającego w oparciu o logikę rozmytą wzorowana była na klasycznym liniowym regulatorze PID. Regulator neuronowy jest równoważnikiem regulatora rozmytego zbudowanym w oparciu o sztuczną sieć neuronową o radialnych funkcjach bazowych (RBF). Wstępne badania układów sterowania z rozważanymi regulatorami wykonane zostały w środowisku obliczeniowym MATLAB/Simulink z użyciem modeli symulacyjnych. Badania docelowe przeprowadzone były w układzie fizycznym, w którym algorytmy sterowania zaprogramowane zostały w mikrokontrolerze sygnałowym TMS320F28335, wykorzystanym do automatycznego sterowania poziomem wody w dolnym zbiorniku. Przy porównaniu uzyskanych wyników pod uwagę wzięty został również klasyczny regulator liniowy PID.
This paper presents controllers built according to the methods of artificial intelligence. The classic PID controller used to control the level of water in a cascade of two tanks was replaced with regulators: fuzzy and neural. The structure of fuzzy controller acting on the fuzzy logic was base on a classical linear PID controller. A neural controller is equivalent to a fuzzy controller based on artificial neural network having radial base functions (RBF). Preliminary testing of control systems with the controllers considered were made in computing simulation MATLAB/Simulink. The final investigations were conducted in the target physical system in which the control algorithms were programmed in the signal processor TMS320F28335, used for automatic control of the water level in the lower tank. In comparing the results obtained the classic linear PID controller was considered.
Źródło:
Zeszyty Naukowe Akademii Marynarki Wojennej; 2012, R. 53 nr 3 (190), 3 (190); 123-138
0860-889X
Pojawia się w:
Zeszyty Naukowe Akademii Marynarki Wojennej
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Inversion of fuzzy neural networks for the reduction of noise in the control loop for automotive applications
Autorzy:
Nentwig, M.
Mercorelli, P.
Powiązania:
https://bibliotekanauki.pl/articles/384669.pdf
Data publikacji:
2009
Wydawca:
Sieć Badawcza Łukasiewicz - Przemysłowy Instytut Automatyki i Pomiarów
Tematy:
neural networks
fuzzy control
inversion of neural networks
automotive control
noise reduction
Opis:
A robust throttle valve control has been an attractive problem since throttle by wire systems were established in the mid-nineties. Control strategies often use a feed-forward controller which use an inverse model; however, mathematical model inversions imply a high order of differentiation of the state variables resulting in noise effects. In general, neural networks are a very effective and popular tool for modelling. The inversion of a neural network makes it possible to use these networks in control problem schemes. This paper presents a control strategy based upon an inversion of a feed-forward trained local linear model tree. The local linear model tree is realized through a fuzzy neural network. Simulated results from real data measurements are presented, and two control loops are explicitly compared.
Źródło:
Journal of Automation Mobile Robotics and Intelligent Systems; 2009, 3, 3; 83-89
1897-8649
2080-2145
Pojawia się w:
Journal of Automation Mobile Robotics and Intelligent Systems
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Elman neural network for modeling and predictive control of delayed dynamic systems
Autorzy:
Wysocki, A.
Ławryńczuk, M.
Powiązania:
https://bibliotekanauki.pl/articles/229646.pdf
Data publikacji:
2016
Wydawca:
Polska Akademia Nauk. Czytelnia Czasopism PAN
Tematy:
dynamic models
process control
model predictive control
neural networks
Elman neural network
delayed systems
Opis:
The objective of this paper is to present a modified structure and a training algorithm of the recurrent Elman neural network which makes it possible to explicitly take into account the time-delay of the process and a Model Predictive Control (MPC) algorithm for such a network. In MPC the predicted output trajectory is repeatedly linearized on-line along the future input trajectory, which leads to a quadratic optimization problem, nonlinear optimization is not necessary. A strongly nonlinear benchmark process (a simulated neutralization reactor) is considered to show advantages of the modified Elman neural network and the discussed MPC algorithm. The modified neural model is more precise and has a lower number of parameters in comparison with the classical Elman structure. The discussed MPC algorithm with on-line linearization gives similar trajectories as MPC with nonlinear optimization repeated at each sampling instant.
Źródło:
Archives of Control Sciences; 2016, 26, 1; 117-142
1230-2384
Pojawia się w:
Archives of Control Sciences
Dostawca treści:
Biblioteka Nauki
Artykuł
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