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Wyświetlanie 1-15 z 122
Tytuł:
A study of dynamic hysteresis model for a magnetorheological damper
Autorzy:
Chen, Kun-Yung
Kung, Ming-Lung
Powiązania:
https://bibliotekanauki.pl/articles/2200888.pdf
Data publikacji:
2023
Wydawca:
Polskie Towarzystwo Mechaniki Teoretycznej i Stosowanej
Tematy:
identification
LuGre model
magnetorheological damper
self-learning particle
swarm optimization
SLPSO
sliding mode observer
SMO
Opis:
The paper proposes a new dynamic model based on the LuGre model and an electrical equation to describe the hysteresis phenomenon for a magnetorheological (MR) damper. In addition, a sliding mode observer (SMO) is proposed to estimate unmeasurable states of the MR damper. The parameters of the MR damper are successfully identified by using the self-learning particle swarm optimization (SLPSO) algorithm. The contributions of this paper are: i) a new dynamic model based on the LuGre model and an electrical equation for an MR damper is successfully formulated to fit for the hysteresis behavior, ii) the exerted damping force can be practically adjusted by using input voltage for the dynamic model, iii) the SMO is proposed to estimate the internal states and current, and iv) the unknown parameters of the MR damper are successfully identified by using the SLPSO algorithm with a numerical experiment.
Źródło:
Journal of Theoretical and Applied Mechanics; 2023, 61, 2; 259--274
1429-2955
Pojawia się w:
Journal of Theoretical and Applied Mechanics
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Applied TVA-PSO for optimal energy efficient integration of renewable energy sources based maximizing TEC levels
Autorzy:
Lasmari, Adel
Zellagui, Mohamed
Powiązania:
https://bibliotekanauki.pl/articles/41186676.pdf
Data publikacji:
2023
Wydawca:
Politechnika Warszawska, Instytut Techniki Cieplnej
Tematy:
renewable distributed generation
optimal energy efficient integration
electric distribution networks
TEC levels
time-varying acceleration particle swarm optimization
odnawialna generacja
optymalna integracja
dystrybucja energii elektrycznej
optymalizacja
Opis:
After the rapid increase in the population demography and industrial revolution, many researchers focus on maintaining the balance between the consumption and the production; in this regard, decentralized production plays an important role to achieve this balance, because of its technical economic aspect such as power losses reduction and voltage profile improvement. These advantages can better exploited through the optimal assessment of Distributed Generation (DG). This paper is interested in the study of the optimal location and size of one and multiple DG based on photovoltaic solar sources PV-DG in Radial Distribution Network (RDN) using the Time Varying Acceleration Particle Swarm Optimization Algorithm (TVA-PSO). This algorithm implemented to maximize the Multi-Objective Functions (MOF) based on the Environmental Pollution Reduction Level (EPRL), the Voltage Deviation Level (VDL), Active Power Loss Level (APLL), the Net Saving Level (NSL), and finally the Short Circuit Level (SCL). The proposed method is tested on the standard IEEE 33-, 69-and 118-bus RDN. Outcomes proves that the proposed TVA-PSO is more efficient to solve the optimal allocation of multiple DGs with high convergence rate and minimum power loss reduction.
Źródło:
Journal of Power Technologies; 2023, 103, 2; 123-137
1425-1353
Pojawia się w:
Journal of Power Technologies
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
ARL-Wavelet-BPF optimization using PSO algorithm for bearing fault diagnosis
Autorzy:
Ahsan, Muhammad
Bismor, Dariusz
Manzoor, Muhammad Arslan
Powiązania:
https://bibliotekanauki.pl/articles/27322619.pdf
Data publikacji:
2023
Wydawca:
Polska Akademia Nauk. Czasopisma i Monografie PAN
Tematy:
signal-to-noise ratio
asymmetric real Laplace wavelet
bandpass filter
particle swarm optimization
spectral kurtosis
fault frequency
Opis:
Rotating element bearings are the backbone of every rotating machine. Vibration signals measured from these bearings are used to diagnose the health of the machine, but when the signal-to-noise ratio is low, it is challenging to diagnose the fault frequency. In this paper, a new method is proposed to enhance the signal-to-noise ratio by applying the Asymmetric Real Laplace wavelet Bandpass Filter (ARL-wavelet-BPF). The Gaussian function of the ARL-wavelet represents an excellent BPF with smooth edges which helps to minimize the ripple effects. The bandwidth and center frequency of the ARL-wavelet-BPF are optimized using the Particle Swarm Optimization (PSO) algorithm. Spectral kurtosis (SK) of the envelope spectrum is employed as a fitness function for the PSO algorithm which helps to track the periodic spikes generated by the fault frequency in the vibration signal. To validate the performance of the ARL-wavelet-BPF, different vibration signals with low signal-to-noise ratio are used and faults are diagnosed.
Źródło:
Archives of Control Sciences; 2023, 33, 3; 589--606
1230-2384
Pojawia się w:
Archives of Control Sciences
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Computer-aided system for layout of fire hydrants on boards designed vessel using the Particle Swarm Optimization algorithm
Autorzy:
Gomułka, Piotr
Powiązania:
https://bibliotekanauki.pl/articles/34600515.pdf
Data publikacji:
2023
Wydawca:
Politechnika Gdańska. Wydział Inżynierii Mechanicznej i Okrętownictwa
Tematy:
ship
fire hydrant
design
layout
particle swarm optimization
Opis:
The functional layout of fire safety equipment in technical spaces of ships is a time-consuming process. When designing a ship fire protection system, the designer must manually position each system component in such a way as to meet the requirements of regulations arising from the technical specification, various legal regulations of maritime conventions and classification societies of the vessel to be designed. Layout of fire hydrants assisted by a computer that is based on pre-defined criteria and various constraints could significantly support the designer in working easier and faster. This paper presents a prototype computer-aided design system that enables optimal placement of fire hydrants using the metaheuristic Particle Swarm Optimization (PSO) algorithm. This algorithm was used in Rhinoceros 3D software with its Grasshopper plugin for visualizing the arrangement of fire safety equipment. Various solution arrangements compared with the fire hydrant placement in real ships are illustrated by a case study. Demonstrating how design work can be facilitated and what potential benefits can be achieved are presented as well.
Źródło:
Polish Maritime Research; 2023, 4; 4-16
1233-2585
Pojawia się w:
Polish Maritime Research
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Feature optimization using a two-tier hybrid optimizer in an Internet of Things network
Autorzy:
Agrawal, Akhileshwar Prasad
Singh, Nanhay
Powiązania:
https://bibliotekanauki.pl/articles/15548024.pdf
Data publikacji:
2023
Wydawca:
Uniwersytet Zielonogórski. Oficyna Wydawnicza
Tematy:
IoT
Internet of Things
anomaly mitigation
GWO
Gray Wolf Optimizer
feature optimization
PSO
particle swarm optimizer
Internet Rzeczy
optymalizacja funkcji
Opis:
The growing use of the Internet of Things (IoT) in smart applications necessitates improved security monitoring of IoT components. The security of such components is monitored using intrusion detection systems which run machine learning (ML) algorithms to classify access attempts as anomalous or normal. However, in this case, one of the issues is the large length of the data feature vector that any ML or deep learning technique implemented on resource-constrained intelligent nodes must handle. In this paper, the problem of selecting an optimal-feature set is investigated to reduce the curse of data dimensionality. A two-layered approach is proposed: the first tier makes use of a random forest while the second tier uses a hybrid of gray wolf optimizer (GWO) and the particle swarm optimizer (PSO) with the k-nearest neighbor as the wrapper method. Further, differential weight distribution is made to the local-best and global-best positions in the velocity equation of PSO. A new metric, i.e., the reduced feature to accuracy ratio (RFAR), is introduced for comparing various works. Three data sets, namely, NSLKDD, DS2OS and BoTIoT, are used to evaluate and validate the proposed work. Experiments demonstrate improvements in accuracy up to 99.44%, 99.44% and 99.98% with the length of the optimal-feature vector equal to 9, 4 and 8 for the NSLKDD, DS2OS and BoTIoT data sets, respectively. Furthermore, classification improves for many of the individual classes of attacks: denial-of-service (DoS) (99.75%) and normal (99.52%) for NSLKDD, malicious control (100%) and DoS (68.69%) for DS2OS, and theft (95.65%) for BoTIoT.
Źródło:
International Journal of Applied Mathematics and Computer Science; 2023, 33, 2; 313--326
1641-876X
2083-8492
Pojawia się w:
International Journal of Applied Mathematics and Computer Science
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Maintenance policy of degradation components based on the two-phase Wiener process
Autorzy:
Song, Minqiao
Zhang, Yingzhi
Yang, Fang
Wang, Xiaofeng
Guo, Guiming
Powiązania:
https://bibliotekanauki.pl/articles/28328273.pdf
Data publikacji:
2023
Wydawca:
Polska Akademia Nauk. Polskie Naukowo-Techniczne Towarzystwo Eksploatacyjne PAN
Tematy:
condition-based maintenance
two-phase inspection
two-phase Wiener process
Particle Swarm Optimization
sensitivity analysis
Opis:
This paper proposes a condition-based maintenance policy for the two-phase Wiener degradation process components. The main contribution of this article is to provide the time distribution of degradation failures for the two-phase Wiener process degradationcomponent, as well as the modeling and solving methods for two-phase maintenance. The two-phase maintenance policy includes two-phase inspection and preventive replacement maintenance operations. The established optimization maintenance policy model aims to minimize long-term operation costs. The specific cost calculation equation and the solution method of the maintenance model are given. The feasibility of the maintenance policy model is verified using the two-phase degradation data of the Liquid Coupling Devices. The Particle swarm optimization algorithm can stably solve the described problem, and the results show that the two-phase maintenance policy can be more economical and improve components availability. After that, we also analyzed the impact of the cost parameters on the maintenance policy through sensitivity analysis.
Źródło:
Eksploatacja i Niezawodność; 2023, 25, 4; art. no. 172537
1507-2711
Pojawia się w:
Eksploatacja i Niezawodność
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Modelling Microcystis Cell Density in a Mediterranean Shallow Lake of Northeast Algeria (Oubeira Lake), Using Evolutionary and Classic Programming
Autorzy:
Arif, Salah
Djellal, Adel
Djebbari, Nawel
Belhaoues, Saber
Touati, Hassen
Guellati, Fatma Zohra
Bensouilah, Mourad
Powiązania:
https://bibliotekanauki.pl/articles/2174666.pdf
Data publikacji:
2023
Wydawca:
Akademia Górniczo-Hutnicza im. Stanisława Staszica w Krakowie. Wydawnictwo AGH
Tematy:
microcystis cell density
Multiple Linear Regression
Support Vector Machine
Particle Swarm Optimization
Genetic Algorithm
Bird Swarm Algorithm
Opis:
Caused by excess levels of nutrients and increased temperatures, freshwater cyanobacterial blooms have become a serious global issue. However, with the development of artificial intelligence and extreme learning machine methods, the forecasting of cyanobacteria blooms has become more feasible. We explored the use of multiple techniques, including both statistical [Multiple Regression Model (MLR) and Support Vector Machine (SVM)] and evolutionary [Particle Swarm Optimization (PSO), Genetic Algorithm (GA), and Bird Swarm Algorithm (BSA)], to approximate models for the prediction of Microcystis density. The data set was collected from Oubeira Lake, a natural shallow Mediterranean lake in the northeast of Algeria. From the correlation analysis of ten water variables monitored, six potential factors including temperature, ammonium, nitrate, and ortho-phosphate were selected. The performance indices showed; MLR and PSO provided the best results. PSO gave the best fitness but all techniques performed well. BSA had better fitness but was very slow across generations. PSO was faster than the other techniques and at generation 20 it passed BSA. GA passed BSA a little further, at generation 50. The major contributions of our work not only focus on the modelling process itself, but also take into consideration the main factors affecting Microcystis blooms, by incorporating them in all applied models.
Źródło:
Geomatics and Environmental Engineering; 2023, 17, 2; 31--68
1898-1135
Pojawia się w:
Geomatics and Environmental Engineering
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Optimization of Square-shaped Bolted Joints Based on Improved Particle Swarm Optimization Algorithm
Autorzy:
Chen, Kui
Yang, Cheng
Zhao, Yongsheng
Niu, Peng
Niu, NaNa
Hongchao, Wu
Powiązania:
https://bibliotekanauki.pl/articles/27312779.pdf
Data publikacji:
2023
Wydawca:
Polska Akademia Nauk. Polskie Naukowo-Techniczne Towarzystwo Eksploatacyjne PAN
Tematy:
particle swarm optimization algorithm
bolt connection
bolted joint
fractal theory
Opis:
The bolted joint is widely used in heavy-duty CNC machine tools, which has huge influence on working precision and overall stiffness of CNC machine. The process parameters of group bolt assembly directly affect the stiffness of the connected parts. The dynamic model of bolted joints is established based on the fractal theory, and the overall stiffness of joint surface is calculated. In order to improve the total stiffness of bolted assembly, an improved particle swarm optimization algorithm with combination of time-varying weights and contraction factor is proposed. The input parameters are preloading of bolts, fractal dimension, roughness, and object thickness. The main goal is to maximize the global rigidity. The optimization results show that improved algorithm has better convergence, faster calculation speed, preferable results, and higher optimization performance than standard particle swarm optimization algorithm. Moreover, the global rigidity optimization is achieved.
Źródło:
Eksploatacja i Niezawodność; 2023, 25, 3; art. no. 168487
1507-2711
Pojawia się w:
Eksploatacja i Niezawodność
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Parameter estimation of photovoltaic module relied on golden jackal optimization
Autorzy:
Nguyen, Thuan Thanh
Powiązania:
https://bibliotekanauki.pl/articles/27309949.pdf
Data publikacji:
2023
Wydawca:
Polska Akademia Nauk. Czasopisma i Monografie PAN
Tematy:
golden jackal optimization
henry gas solubility optimization
particle swarm optimization
PV parameter estimation
single diode model
Opis:
Due to the nonlinear current-voltage (I-V) relationship of the photovoltaic (PV) module, building a precise mathematical model of the PV module is necessary for evaluating and optimizing the PV systems. This paper proposes a method of building PV parameter estimation models based on golden jackal optimization (GJO). GJO is a recently developed algorithm inspired by the idea of the hunting behavior of golden jackals. The explored and exploited searching strategies of GJO are built based on searching for prey as well as harassing and grabbing prey of golden jackals. The performance of GJO is considered on the commercial KC200GT module under various levels of irradiance and temperature. Its performance is compared to well-known particle swarm optimization (PSO), recent Henry gas solubility optimization (HGSO) and some previous methods. The obtained results show that GJO can estimate unknown PV parameters with high precision. Furthermore, GJO can also provide better efficiency than PSO and HGSO in terms of statistical results over several runs. Thus, GJO can be a reliable algorithm for the PV parameter estimation problem under different environmental conditions.
Źródło:
Archives of Electrical Engineering; 2023, 72, 4; 987--1003
1427-4221
2300-2506
Pojawia się w:
Archives of Electrical Engineering
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Photovoltaic power prediction based on improved grey wolf algorithm optimized back propagation
Autorzy:
He, Ping
Dong, Jie
Wu, Xiaopeng
Yun, Lei
Yang, Hua
Powiązania:
https://bibliotekanauki.pl/articles/27309934.pdf
Data publikacji:
2023
Wydawca:
Polska Akademia Nauk. Czasopisma i Monografie PAN
Tematy:
BP neural network
photovoltaic power generation
PSO–GWO model
PSO–GWO–BP prediction model
particle swarm optimization
gray wolf optimization
back propagation
standard grey wolf algorithm
Opis:
At present, the back-propagation (BP) network algorithm widely used in the short-term output prediction of photovoltaic power stations has the disadvantage of ignoring meteorological factors and weather conditions in the input. The existing traditional BP prediction model lacks a variety of numerical optimization algorithms, such that the prediction error is large. The back-propagation (BP) neural network is easy to fall into local optimization thus reducing the prediction accuracy in photovoltaic power prediction. In order to solve this problem, an improved grey wolf optimization (GWO) algorithm is proposed to optimize the photovoltaic power prediction model of the BP neural network. So, an improved grey wolf optimization algorithm optimized BP neural network for a photovoltaic (PV) power prediction model is proposed. Dynamic weight strategy, tent mapping and particle swarm optimization (PSO) are introduced in the standard grey wolf optimization (GWO) to construct the PSO–GWO model. The relative error of the PSO–GWO–BP model predicted data is less than that of the BP model predicted data. The average relative error of PSO–GWO–BP and GWO–BP models is smaller, the average relative error of PSO–GWO–BP model is the smallest, and the prediction stability of the PSO–GWO–BP model is the best. The model stability and prediction accuracy of PSO–GWO–BP are better than those of GWO–BP and BP.
Źródło:
Archives of Electrical Engineering; 2023, 72, 3; 613--628
1427-4221
2300-2506
Pojawia się w:
Archives of Electrical Engineering
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Research on preventive maintenance strategy of Coating Machine based on dynamic failure rate
Autorzy:
Gu, Dongwei
Nie, Ruihua
Han, Wenbo
Chen, Guang
Jia, Ligang
Powiązania:
https://bibliotekanauki.pl/articles/24200819.pdf
Data publikacji:
2023
Wydawca:
Polska Akademia Nauk. Polskie Naukowo-Techniczne Towarzystwo Eksploatacyjne PAN
Tematy:
dynamic preventive maintenance model
DM
BP-LSTM
dynamic failure rate threshold
preventive maintenance strategy
Genetic-Particle Swarm Optimization
GPSO
Opis:
In this paper, a dynamic preventive maintenance strategy is proposed for the problem of high maintenance cost rate due to excessive maintenance caused by unreasonable maintenance threshold setting when complex electromechanical equipment maintenance strategy is formulated. Increasing failure rate factor and decreasing service age factor are introduced to describe the evolution rules of failure rate during the maintenance of the coating machine, and the BP-LSTM (BP-Long Short Term Memory Network, BP-LSTM) model is combined to predict the failure rate of the coating machine. A Dynamic preventive maintenance Model (DM) that relies on dynamic failure rate thresholds to classify the three preventive maintenance modes of minor, medium and major repairs is constructed. A dynamic preventive maintenance strategy optimization process based on Genetic-Particle Swarm Optimization (GPSO) algorithm with the lowest cost rate per unit time in service phase is built to solve the difficult problem of dynamic failure rate threshold finding. Based on the historical operating data of the coating machine, a case study of the dynamic preventive maintenance strategy of the coating machine was conducted to verify the effectiveness of the model and the developed maintenance strategy proposed in this paper. The results show that the maintenance strategy developed in this paper can ensure better economy and applicability.
Źródło:
Eksploatacja i Niezawodność; 2023, 25, 1; atr. no. 20
1507-2711
Pojawia się w:
Eksploatacja i Niezawodność
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Research on the mill feeding system of an elastic variable universe fuzzy control based on particle swarm optimization algorithm
Autorzy:
Tian, Niu
Huang, Songwei
He, Lifang
Du, Lingpan
Yang, Sheping
Huang, Bin
Powiązania:
https://bibliotekanauki.pl/articles/24085898.pdf
Data publikacji:
2023
Wydawca:
Politechnika Wrocławska. Oficyna Wydawnicza Politechniki Wrocławskiej
Tematy:
fuzzy control
contraction-expansion factor
particle swarm optimization
Opis:
The grinding process in the concentrator is a part of the largest energy consumption, but also the most likely to cause a waste of resources, so the optimization of the grinding process is a very important link.The traditional fuzzy controller relies solely on the expert knowledge summary to construct control rules, which can cause significant steady-state errors in the model. In order to solve the above problem, this paper proposes an elastic variable universe fuzzy control based on Particle Swarm Optimization (PSO) algorithm. The elastic universe fuzzy control model does not need precise fuzzy rules, but only needs to input the general trend of the rules, and the division of the universe is performed by the contraction-expansionfactor. The control performance is directly related to the contraction-expansionfactor, so this article also proposes using particle swarm optimization to optimize the scaling factor to achieve the optimal value. Finally, simulation models of traditional fuzzy control and elastic universe fuzzy control of feeding system of mill were built using Python to verify the control effect. Itssimulation results show that the time of the reaction of the fuzzy control system in the elastic variable theory universe based on particle swarm optimization was shorter by 34.48% comparing to the traditional one. Elastic variable universe fuzzy control based on particle swarm optimization (PSO) effectively improved the control accuracy of the mill feeding system and improved the response speed of the system to a certain extent.
Źródło:
Physicochemical Problems of Mineral Processing; 2023, 59, 3; art. no. 169942
1643-1049
2084-4735
Pojawia się w:
Physicochemical Problems of Mineral Processing
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Bainite transformation time model optimization for Austempered Ductile Iron with the use of heuristic algorithms
Autorzy:
Olejarczyk-Wożeńska, Izabela
Opaliński, Andrzej
Mrzygłód, Barbara
Regulski, Krzysztof
Kurowski, Wojciech
Powiązania:
https://bibliotekanauki.pl/articles/29520068.pdf
Data publikacji:
2022
Wydawca:
Akademia Górniczo-Hutnicza im. Stanisława Staszica w Krakowie. Wydawnictwo AGH
Tematy:
heuristic optimization
bainite
ADI
Particle Swarm Optimization
Evolutionary Optimization Algorithm
Opis:
The paper presents the application of heuristic optimization methods in identifying the parameters of a model for bainite transformation time in ADI (Austempered Ductile Iron). Two algorithms were selected for parameter optimization – Particle Swarm Optimization and Evolutionary Optimization Algorithm. The assumption of the optimization process was to obtain the smallest normalized mean square error (objective function) between the time calculated on the basis of the identified parameters and the time derived from the experiment. As part of the research, an analysis was also made in terms of the effectiveness of selected methods, and the best optimization strategies for the problem to be solved were selected on their basis.
Źródło:
Computer Methods in Materials Science; 2022, 22, 3; 125-136
2720-4081
2720-3948
Pojawia się w:
Computer Methods in Materials Science
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Computationally efficient nonlinear model predictive controller using parallel particle swarm optimization
Autorzy:
Diwan, Supriya P.
Deshpande, Shraddha S.
Powiązania:
https://bibliotekanauki.pl/articles/2173694.pdf
Data publikacji:
2022
Wydawca:
Polska Akademia Nauk. Czytelnia Czasopism PAN
Tematy:
nonlinear model predictive control
particle swarm optimization
PSO
fast dynamic systems
rotary inverted pendulum
divide approach
conquer approach
kontrola predykcyjna modelu nieliniowa
optymalizacja roju cząstek
system dynamiczny szybki
wahadło obrotowe odwrócone
Opis:
As nonlinear optimization techniques are computationally expensive, their usage in the real-time era is constrained. So this is the main challenge for researchers to develop a fast algorithm that is used in real-time computations. This work proposes a fast nonlinear model predictive control approach based on particle swarm optimization for nonlinear optimization with constraints. The suggested algorithm divide and conquer technique improves computing speed and disturbance rejection capability, demonstrating its suitability for real-time applications. The performance of this approach under constraints is validated using a highly nonlinear fast and dynamic real-time inverted pendulum system. The solution presented through work is computationally feasible for smaller sampling times and it gives promising results compared to the state of art PSO algorithm
Źródło:
Bulletin of the Polish Academy of Sciences. Technical Sciences; 2022, 70, 4; art. no. e140696
0239-7528
Pojawia się w:
Bulletin of the Polish Academy of Sciences. Technical Sciences
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Jacking and energy consumption control over network for jack-up rig: simulation and experiment
Autorzy:
Do, Viet-Dung
Dang, Xuan-Kien
Tran, Tien-Dat
Pham, Thi Duyen-Anh
Powiązania:
https://bibliotekanauki.pl/articles/32912855.pdf
Data publikacji:
2022
Wydawca:
Politechnika Gdańska. Wydział Inżynierii Mechanicznej i Okrętownictwa
Tematy:
networked control system
environmental forces
energy consumption
Fuzzy Particle Swarm Optimization
jacking system
time-delay
Opis:
Oil and gas projects differ from regular investment projects in that they are frequently large-scale, categorised as vital national projects, highly technological, and associated with significant risks. Drilling rigs are a crucial component of the oil and gas sector and the majority of the systems and equipment aboard drilling rigs are operated automatically. Consequently, it is crucial to address the topic of an advanced control theory for off-shore systems. Network technology connected to control is progressively being used to replace outdated technologies, together with other contemporary technologies. In this study, we examine how to adapt a networked control jacking system to the effects of internal and external disturbances with a time delay, using a Fuzzy controller (FC)-based particle swarm optimisation. To demonstrate the benefit of the proposed approach, the developed Fuzzy Particle Swarm Optimisation (FPSO) controller is compared with the fuzzy controller. Finally, the results from simulations and experiments utilising Matlab software and embedded systems demonstrate the suitability of the proposed approach.
Źródło:
Polish Maritime Research; 2022, 3; 89-98
1233-2585
Pojawia się w:
Polish Maritime Research
Dostawca treści:
Biblioteka Nauki
Artykuł
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