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Wyświetlanie 1-10 z 164
Tytuł:
Passivity-based optimal control of discrete-time nonlinear systems
Autorzy:
Binazadeh, T.
Shafiei, M. H.
Powiązania:
https://bibliotekanauki.pl/articles/205917.pdf
Data publikacji:
2013
Wydawca:
Polska Akademia Nauk. Instytut Badań Systemowych PAN
Tematy:
nonlinear discrete-time systems optimal passivity-based control
genetic optimization algorithm
Opis:
In this paper, a passivity-based optimal controlmethod for a broad class of nonlinear discrete-time systems is proposed. The resulting control law is a static output feedback law which is practically preferred with respect to the state feedback law and is simple to implement. The control law has a general structure with adjustable parameters which are tuned, using an optimization method (genetic algorithm), to minimize an arbitrary cost function. By choosing this cost function it is possible to shape the transient response of the closed-loop system, as it is desirable. An illustrative ex ample shows the effectiveness of the proposed approach.
Źródło:
Control and Cybernetics; 2013, 42, 3; 627-637
0324-8569
Pojawia się w:
Control and Cybernetics
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
A simplistic regression-based genetic algorithm optimization of tool-work interface temperature
Autorzy:
Utkarsh
Kumar, Saumya
Kumar, Saaransh
Kiran, G. Uday
Mukhopadhyay, Arkadeb
Barman, Manik
Powiązania:
https://bibliotekanauki.pl/articles/38884187.pdf
Data publikacji:
2022
Wydawca:
Instytut Podstawowych Problemów Techniki PAN
Tematy:
turning
tool-work thermocouple
cutting temperature
ANOVA
Opis:
This work aims to investigate the average tool-work interface temperature for the HSS tool and AISI 1040 steel pair. A tool-work thermocouple is proposed for the measurement of temperature because of its simple construction in addition to the low cost. The machining process of AISI 1040 steel is considered due to its extensive application, including industry usage. The changes in cutting temperature are studied for combinations of cutting speed, feed and the depth of cut during turning operation. The orthogonal array L9 by Taguchi is adopted for designing the experiments within a restricted set of runs. The average cutting temperature shows an increasing curve with functions of speed versus depth of cut and speed versus feed. But no clear trend is observed for a combination of feed versus depth of cut. A second-order regression equation with reasonable accuracy (R2 = 0.99) is fitted using the data. Analysis of variance (ANOVA) reveals the highest contribution from cutting speed, which influences average temperature at the interface of tool and work. Further, the genetic algorithm predicts an optimal combination of parameters, which is 82.542 m/min cutting speed, 0.276 mm/rev feed rate and 0.2 mm depth.
Źródło:
Engineering Transactions; 2022, 70, 2; 141-156
0867-888X
Pojawia się w:
Engineering Transactions
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Automatic identification of malfunctions of large turbomachinery during transient states with genetic algorithm optimization
Autorzy:
Barszcz, Tomasz
Zabaryłło, Mateusz
Powiązania:
https://bibliotekanauki.pl/articles/2052104.pdf
Data publikacji:
2022
Wydawca:
Polska Akademia Nauk. Czytelnia Czasopism PAN
Tematy:
machine learning
fault detection
transient
turbine generator
genetic algorithm
Opis:
Turbines and generators operating in the power generation industry are a major source of electrical energy worldwide. These are critical machines and their malfunctions should be detected in advance in order to avoid catastrophic failures and unplanned shutdowns. A maintenance strategy which enables to detect malfunctions at early stages of their existence plays a crucial role in facilities using such types of machinery. The best source of data applied for assessment of the technical condition are the transient data measured during start-ups and coast-downs. Most of the proposed methods using signal decomposition are applied to small machines with a rolling element bearing in steady-state operation with a shaft considered as a rigid body. The machines examined in the authors’ research operate above their first critical rotational speed interval and thus their shafts are considered to be flexible and are equipped with a hydrodynamic sliding bearing. Such an arrangement introduces significant complexity to the analysis of the machine behavior, and consequently, analyzing such data requires a highly skilled human expert. The main novelty proposed in the paper is the decomposition of transient vibration data into components responsible for particular failure modes. The method is automated and can be used for identification of turbogenerator malfunctions. Each parameter of a particular decomposed function has its physical representation and can help the maintenance staff to operate the machine properly. The parameters can also be used by the managing personnel to plan overhauls more precisely. The method has been validated on real-life data originating from a 200 MW class turbine. The real-life field data, along with the data generated by means of the commercial software utilized in GE’s engineering department for this particular class of machines, was used as the reference data set for an unbalanced response during the transients in question.
Źródło:
Metrology and Measurement Systems; 2022, 29, 1; 175-190
0860-8229
Pojawia się w:
Metrology and Measurement Systems
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
A new auto adaptive fuzzy hybrid particle swarm optimization and genetic algorithm
Autorzy:
Dziwiński, Piotr
Bartczuk, Łukasz
Paszkowski, Józef
Powiązania:
https://bibliotekanauki.pl/articles/1837533.pdf
Data publikacji:
2020
Wydawca:
Społeczna Akademia Nauk w Łodzi. Polskie Towarzystwo Sieci Neuronowych
Tematy:
hybrid methods
Particle Swarm Optimization
Genetic Algorithm
fuzzy systems
multimodal function
Opis:
The social learning mechanism used in the Particle Swarm Optimization algorithm allows this method to converge quickly. However, it can lead to catching the swarm in the local optimum. The solution to this issue may be the use of genetic operators whose random nature allows them to leave this point. The degree of use of these operators can be controlled using a neuro-fuzzy system. Previous studies have shown that the form of fuzzy rules should be adapted to the fitness landscape of the problem. This may suggest that in the case of complex optimization problems, the use of different systems at different stages of the algorithm will allow to achieve better results. In this paper, we introduce an auto adaptation mechanism that allows to change the form of fuzzy rules when solving the optimization problem. The proposed mechanism has been tested on benchmark functions widely adapted in the literature. The results verify the effectiveness and efficiency of this solution.
Źródło:
Journal of Artificial Intelligence and Soft Computing Research; 2020, 10, 2; 95-111
2083-2567
2449-6499
Pojawia się w:
Journal of Artificial Intelligence and Soft Computing Research
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Adaptive robust PID sliding control of a liquid level system based on multi-objective genetic algorithm optimization
Autorzy:
Mahmoodabadi, M. J.
Taherkhorsandi, M.
Talebipour, M.
Powiązania:
https://bibliotekanauki.pl/articles/206697.pdf
Data publikacji:
2017
Wydawca:
Polska Akademia Nauk. Instytut Badań Systemowych PAN
Tematy:
sliding mode control
PID control
adaptive control
genetic algorithm
multi-objective optimization
liquid level system
Opis:
Adaptive robust PID sliding mode control optimized by means of multi-objective genetic algorithm is presented in this paper to control a three-tank liquid level system with external disturbances. While PID constitutes a reliable and stable controller, when compared to sliding mode control (SMC); robustness and tracking performance of SMC are higher than those of the PID control. To use the unique features of both controllers, optimal sliding mode control is executed in terms of a supervisory controller to enhance the performance of optimal adaptive PID control and to provide the necessary control inputs. After the design of the control law, control coefficients of all four involved controllers are optimized by using the multi-objective genetic algorithm so as to minimize errors and the input of the controller. Simulations illustrate that the adaptive robust PID sliding controller based on multi-objective genetic algorithm optimization provides a superior response in comparison to the results obtained separately by PID control, sliding mode control, and adaptive PID control, respectively.
Źródło:
Control and Cybernetics; 2017, 46, 3; 227-246
0324-8569
Pojawia się w:
Control and Cybernetics
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Application of genetic algorithm for optimization the safety system of the nuclear reactor
Autorzy:
El-Sayed Wahed, M.
Ibrahim, W. Z.
Effat, A. M.
Powiązania:
https://bibliotekanauki.pl/articles/146492.pdf
Data publikacji:
2009
Wydawca:
Instytut Chemii i Techniki Jądrowej
Tematy:
genetic algorithm
non-dominated sorting
chimney water injection system (CWIS)
Egypt nuclear
Opis:
The purpose of this paper is to present an approach to optimization in which every target is considered as a separate objective to be optimized. Multi-objective optimization is a powerful tool for resolving conflicting objectives in engineering design and numerous other fields. One approach to solve multi-objective optimization problems is the non-dominated sorting genetic algorithm (NSGA). Genetic algorithm (GA) was applied in regarding the choice of the time intervals for the periodic testing of the components of the chimney water injection system (CWIS) of the 22 MW open pool multipurpose reactor (MPR), ETRR-2, at the Egyptian Atomic Energy Authority, has been used as a case study.
Źródło:
Nukleonika; 2009, 54, 1; 51-56
0029-5922
1508-5791
Pojawia się w:
Nukleonika
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
A Comparative Study of PID Controller Tuning Using GA, EP, PSO and ACO
Autorzy:
Nagaraj, B.
Vijayakumar, P.
Powiązania:
https://bibliotekanauki.pl/articles/384767.pdf
Data publikacji:
2011
Wydawca:
Sieć Badawcza Łukasiewicz - Przemysłowy Instytut Automatyki i Pomiarów
Tematy:
ant colony algorithm
evolutionary program
genetic algorithm particle swarm optimization and soft computing
Opis:
Proportional - Integral - Derivative control schemes continue to provide the simplest and effective solutions to most of the control engineering applications today. How ever PID controller are poorly tuned in practice with most of the tuning done manually which is difficult and time consuming. This article comes up with a hybrid approach involving Genetic Algorithm (GA), Evolutionary Pro gramming (EP), Particle Swarm Optimization (PSO) and Ant Colony Optimization (ACO). The proposed hybrid algorithm is used to tune the PID parameters and its per formance has been compared with the conventional me thods like Ziegler Nichols and Cohen Coon method. The results obtained reflect that use of heuristic algorithm based controller improves the performance of process in terms of time domain specifications, set point tracking, and regulatory changes and also provides an optimum stability. Speed control of DC motor process is used to assess the efficacy of the heuristic algorithm methodology
Źródło:
Journal of Automation Mobile Robotics and Intelligent Systems; 2011, 5, 2; 42-48
1897-8649
2080-2145
Pojawia się w:
Journal of Automation Mobile Robotics and Intelligent Systems
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Klasyczny algorytm genetyczny w dynamicznej optymalizacji modelu liniowego produkcji roślinnej
Classical genetic algorithm for dynamic optimization of the linear model of plants production
Autorzy:
Landowska, A.
Powiązania:
https://bibliotekanauki.pl/articles/78542.pdf
Data publikacji:
2011
Wydawca:
Zachodniopomorski Uniwersytet Technologiczny w Szczecinie. Wydawnictwo Uczelniane ZUT w Szczecinie
Opis:
Article presents application of classical genetic algorithm for the problem of dynamic optimization of the linear model. The model describes plants production during two years and takes into consideration plants changing. The soil in a good culture it is very important issue to obtain the highest crop. In the work the conditions for classical genetic algorithm to solving introduced problem are presented.
Źródło:
Folia Pomeranae Universitatis Technologiae Stetinensis. Oeconomica; 2011, 62
2081-0644
Pojawia się w:
Folia Pomeranae Universitatis Technologiae Stetinensis. Oeconomica
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Efektywność modyfikacji algorytmu genetycznego w optymalizacji złożonych systemów oświetlenia elektrycznego
Efficiency of genetic algorithm modification of optimization of the electric light complex systems
Autorzy:
Tomczewski, A.
Powiązania:
https://bibliotekanauki.pl/articles/376404.pdf
Data publikacji:
2013
Wydawca:
Politechnika Poznańska. Wydawnictwo Politechniki Poznańskiej
Tematy:
algorytm genetyczny
oświetlenie elektryczne
oświetlenie wnętrz
optymalizacja kosztów systemu oświetlenia
Opis:
W artykule podjęto tematykę modyfikacji metaheurystyki algorytmu genetycznego wykorzystanego do optymalizacji złożonego systemu oświetlenia wnętrz. Podano charakterystykę zadania, zastosowane kryterium oceny jakości rozwiązań oraz złożoność zagadnienia rzutującą na przebieg procesu optymalizacji. Przedstawiono modyfikacje związane bezpośrednio ze strukturą realizowanego zadania. Wykonano obliczenia optymalizacyjne dla obiektu testowego wykazujące wzrost efektywności metody w stosunku do algorytmu odniesienia.
The article shows the metaheurystics modification of a genetic algorithm used to optimize the lighting complex system. The characteristics of the tasks and the evaluation criterion of the quality of solutions and the complexity of the issues to bear on the process of optimization was presented. Modifications connected to the structure of the task executed was shown. Optimization calculations were performed for the test object, showing an increase of the efficiency of the method compared to a reference algorithm.
Źródło:
Poznan University of Technology Academic Journals. Electrical Engineering; 2013, 73; 173-182
1897-0737
Pojawia się w:
Poznan University of Technology Academic Journals. Electrical Engineering
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Application of genetic algorithm for double-lap adhesive joint design
Autorzy:
Kurennov, Sergei
Barakhov, Konstantin
Polyakov, Olexander
Taranenko, Igor
Powiązania:
https://bibliotekanauki.pl/articles/27309876.pdf
Data publikacji:
2023
Wydawca:
Polska Akademia Nauk. Czasopisma i Monografie PAN
Tematy:
adhesive joint
genetic algorithm
optimization
finite difference method
Goland-Reissner model
złącze klejowe
algorytm genetyczny
optymalizacja
metoda różnic skończonych
Model Golanda-Reissnera
Opis:
The problem of optimal design of symmetrical double-lap adhesive joint is considered. It is assumed that the main plate has constant thickness, while the thickness of the doublers can vary along the joint length. The optimization problem consists in finding optimal length of the joint and an optimal cross-section of the doublers, which provide minimum structural mass at given strength constraints. The classical Goland-Reissner model was used to describe the joint stress state. A corresponding system of differential equations with variable coefficients was solved using the finite difference method. Genetic optimization algorithm was used for numerical solution of the optimization problem. In this case, Fourier series were used to describe doubler thickness variation along the joint length. This solution ensures smoothness of the desired function. Two model problems were solved. It is shown that the length and optimal shape of the doubler depend on the design load.
Źródło:
Archive of Mechanical Engineering; 2023, LXX, 1; 27--42
0004-0738
Pojawia się w:
Archive of Mechanical Engineering
Dostawca treści:
Biblioteka Nauki
Artykuł
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