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Tytuł:
A Time optimal path planning for trajectory tracking of wheeled mobile robots
Autorzy:
Vivekananthan, R.
Karunamoorthy, L.
Powiązania:
https://bibliotekanauki.pl/articles/384765.pdf
Data publikacji:
2011
Wydawca:
Sieć Badawcza Łukasiewicz - Przemysłowy Instytut Automatyki i Pomiarów
Tematy:
wheeled robot
time optimal
trajectory tracking
motion control
fuzzy logic algorithm
Matlab environment
Opis:
Avariety of approaches for trajectory tracking control of wheeled mobile robots have been implemented. This paper presents a model for a time optimal motion control based on fuzzy logic algorithm for a three wheeled nonholonomic mobile robot with desired function. Simplified kinematic equations of a differentially driven robot are designed to follow the path with evaluated linear and angular velocities. Here, the proposed kinematic model is based on a simple geometric approach for getting the desired position and orientation. The speeds are varied depending on the variations in the path and on the posture of the robot. The robot is subjected to move in a constrained workspace. The control architecture was developed based on fuzzy logic algorithm to obtain time optimal motion control of robot trajectory tracking. The kinematic model was done on Matlab software environment and profound impact on the ability of the nonholonomic mobile robot to track the path was evaluated.
Źródło:
Journal of Automation Mobile Robotics and Intelligent Systems; 2011, 5, 2; 35-41
1897-8649
2080-2145
Pojawia się w:
Journal of Automation Mobile Robotics and Intelligent Systems
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Chemical reaction algorithm for type-2 fuzzy control optimization in mobile robots
Autorzy:
de la O, D.
Castillo, O.
Soria, J.
Powiązania:
https://bibliotekanauki.pl/articles/384488.pdf
Data publikacji:
2018
Wydawca:
Sieć Badawcza Łukasiewicz - Przemysłowy Instytut Automatyki i Pomiarów
Tematy:
Chemical Reaction Algorithm
control
fuzzy logic
robotics
Opis:
In this work the optimization process of the tracking and reactive controllers for a mobile robot are presented. The Chemical Reaction Algorithm (CRA) is used to find the optimal parameter values of the membership functions and rules for the reactive and tracking controllers. In this case, we are using five membership functions in each variable of the fuzzy controllers. The main goal of the reactive controller is aimed at providing the robot with the ability to avoid obstacles in its environment. The tests are performed on a benchmark maze problem, in which the goal is not necessarily to leave the maze, but rather that the robot avoids obstacles, in this case the walls, and penalizing for unwanted trajectories, such as cycles. The tracking controller’s goal is for the robot to keep into to a certain path, this in order that the robot can learn to react to unknown environments. The optimization algorithm that was used is based on an abstraction of chemical reactions. To perform the simulation we use the “SimRobot” toolbox, the results of the tests are presented in a detailed fashion, and at the end we are presenting a comparison of results among the CRA, PSO and GA methods.
Źródło:
Journal of Automation Mobile Robotics and Intelligent Systems; 2018, 12, 3; 10-19
1897-8649
2080-2145
Pojawia się w:
Journal of Automation Mobile Robotics and Intelligent Systems
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Optimising a fuzzy fault classification tree by a single-objective genetic algorithm
Autorzy:
Zio, E.
Baraldi, P.
Popescu, I. C.
Powiązania:
https://bibliotekanauki.pl/articles/2069595.pdf
Data publikacji:
2007
Wydawca:
Uniwersytet Morski w Gdyni. Polskie Towarzystwo Bezpieczeństwa i Niezawodności
Tematy:
fault classification
decision tree
fuzzy logic
genetic algorithm
Opis:
In this paper a single-objective Genetic Algorithm is exploited to optimise a Fuzzy Decision Tree for fault classification. The optimisation procedure is presented with respect to an ancillary classification problem built with artificial data. Work is in progress for the application of the proposed approach to a real fault classification problem.
Źródło:
Journal of Polish Safety and Reliability Association; 2007, 2; 391--400
2084-5316
Pojawia się w:
Journal of Polish Safety and Reliability Association
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Intelligent hybrid fuzzy logic system for damage detection of beam-like structural elements
Autorzy:
Sahu, S.
Kumar, P. B.
Parhi, D. R.
Powiązania:
https://bibliotekanauki.pl/articles/281457.pdf
Data publikacji:
2017
Wydawca:
Polskie Towarzystwo Mechaniki Teoretycznej i Stosowanej
Tematy:
fuzzy logic
differential evolution algorithm
crack
natural frequency
Opis:
Fuzzy logic has been used in different research fields for more than three decades. It has become a robust method to solve complex and intricate problems which are otherwise difficult to solve by traditional methods. But it still requires some human experience and knowledge. In the present study, an attempt is made to design a hybrid optimization technique for automatic formation of the fuzzy knowledge based rules using an evolutionary algorithm. This hybridization technique has been applied in the field of damage detection and location of cracks in cracked structural elements. In this paper, a robust fault diagnostic tool based on a differential evolution algorithm and fuzzy logic has been proposed. Theoretical and Finite Element analyzes are done to model the crack and to find the effect of the presence of cracks on changes of vibrational characteristic (natural frequencies) of a fixed-fixed beam. The inputs to DEA-FL system are the first three relative natural frequencies, and the outputs from the system are the relative crack depth and relative crack location. For the validation of the results obtained from the proposed method and to check the robustness of the controller, experimental analysis is performed. To find average error rates, the bootstrap method has been adopted.
Źródło:
Journal of Theoretical and Applied Mechanics; 2017, 55, 2; 509-521
1429-2955
Pojawia się w:
Journal of Theoretical and Applied Mechanics
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
The genetic fuzzy based proactive maintenance of a technical object
Autorzy:
Smoczek, J.
Szpytko, J.
Powiązania:
https://bibliotekanauki.pl/articles/246817.pdf
Data publikacji:
2012
Wydawca:
Instytut Techniczny Wojsk Lotniczych
Tematy:
proactive maintenance
failure prediction
fuzzy logic
genetic algorithm
Opis:
The proactive maintenance is an effective approach to enhance the system availability through real time monitoring the current state of a system. The key part of this method is forecasting the nonoperational states for advanced warning of the failure possibility that can bring the attention of machines operators and maintenance personnel to impending danger facilitate planning preventive and corrective operations, and resources managing as well. The paper presents the HMI/SCADA-type application used to support decision-making process. The proposed approach to proactive maintenance is based on forecasting the remaining useful life of device equipment and delivering the user-defined maintenance strategy developed during system operation. The HMI/SCADA application is used to collect data in form of failures history, changes of operational conditions and performances of a monitored process between failures, as well as heuristic knowledge about process created by experienced user. The data history is used to design the predictive fuzzy models of time between failures of system equipment. The fuzzy predictive models are designed using the genetic algorithm applied to optimize the fuzzy partitions covering the training data examples, as well as to identify fuzzy predictive patterns represented by a set of rules in the knowledge base. The evolutionary learning strategy, which has been proposed in this paper, provides the effective reproduction techniques for searching the solution space with respect to optimization of knowledge base and membership functions according to the fitness function expressed as a ratio of compatibility of fuzzy partitions with data examples to root mean squares error. The proposed application was created and tested on the laboratory stand for monitoring the availability of the overhead travelling crane.
Źródło:
Journal of KONES; 2012, 19, 3; 399-405
1231-4005
2354-0133
Pojawia się w:
Journal of KONES
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Decomposition of the fuzzy inference system for implementation in the FPGA structure
Autorzy:
Wyrwoł, B.
Hrynkiewicz, E.
Powiązania:
https://bibliotekanauki.pl/articles/330759.pdf
Data publikacji:
2013
Wydawca:
Uniwersytet Zielonogórski. Oficyna Wydawnicza
Tematy:
fuzzy logic
fuzzy inference algorithm
decomposition
digital fuzzy logic controller
FPGA
logika rozmyta
algorytm wnioskowania rozmytego
sterownik rozmyty
Opis:
The paper presents the design and implementation of a digital rule-relational fuzzy logic controller. Classical and decomposed logical structures of fuzzy systems are discussed. The second allows a decrease in the hardware cost of the fuzzy system and in the computing time of the final result (fuzzy or crisp), especially when referring to relational systems. The physical architecture consists of IP modules implemented in an FPGA structure. The modules can be inserted into or removed from the project to get a desirable fuzzy logic controller configuration. The fuzzy inference system implemented in FPGA can operate with a much higher performance than software implementations on standard microcontrollers.
Źródło:
International Journal of Applied Mathematics and Computer Science; 2013, 23, 2; 473-483
1641-876X
2083-8492
Pojawia się w:
International Journal of Applied Mathematics and Computer Science
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
K-Means and Fuzzy based Hybrid Clustering Algorithm for WSN
Autorzy:
Angadi, Basavaraj M.
Kakkasageri, Mahabaleshwar S.
Powiązania:
https://bibliotekanauki.pl/articles/27311955.pdf
Data publikacji:
2023
Wydawca:
Polska Akademia Nauk. Czasopisma i Monografie PAN
Tematy:
wireless sensor networks
cluster
K-Means algorithm
fuzzy logic
Opis:
Wireless Sensor Networks (WSN) acquired a lot of attention due to their widespread use in monitoring hostile environments, critical surveillance and security applications. In these applications, usage of wireless terminals also has grown significantly. Grouping of Sensor Nodes (SN) is called clustering and these sensor nodes are burdened by the exchange of messages caused due to successive and recurring re-clustering, which results in power loss. Since most of the SNs are fitted with nonrechargeable batteries, currently researchers have been concentrating their efforts on enhancing the longevity of these nodes. For battery constrained WSN concerns, the clustering mechanism has emerged as a desirable subject since it is predominantly good at conserving the resources especially energy for network activities. This proposed work addresses the problem of load balancing and Cluster Head (CH) selection in cluster with minimum energy expenditure. So here, we propose hybrid method in which cluster formation is done using unsupervised machine learning based kmeans algorithm and Fuzzy-logic approach for CH selection.
Źródło:
International Journal of Electronics and Telecommunications; 2023, 69, 4; 793--801
2300-1933
Pojawia się w:
International Journal of Electronics and Telecommunications
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Comparison of Intelligent Control Methods for the Ore Jigging Process
Autorzy:
Kulakova, Yelena
Wójcik, Waldemar
Suleimenov, Batyrbek
Smolarz, Andrzej
Powiązania:
https://bibliotekanauki.pl/articles/1844513.pdf
Data publikacji:
2021
Wydawca:
Polska Akademia Nauk. Czytelnia Czasopism PAN
Tematy:
neural network
ore jiggling
control algorithm
fuzzy logic
correlation
Opis:
Efficient control of the process of jigging ore of small and fine grain allows avoiding the loss of valuable material in production residual. Due to the multi-dimensionality and multi-connectivity of this enrichment process, classical control methods do not allow achieving the maximum technological indicators of enrichment. This paper proposes investigating intelligent algorithms for controlling the jigging process, which determine the key variables - the level of the natural «bed» and the ripple frequency of the jigging machine. Algorithms are developed using fuzzy logic, neural and hybrid networks. The adequacy of intelligent algorithms was evaluated using the following criteria: correlation of expert and model values (R); Root Mean Square Error (RMSE); Mean absolute percentage error (MAPE). To assess the adequacy of the obtained algorithms, a test sample of input variables, different from the training one, was compiled. As a consequence, we determined an algorithm that gives a minimal discrepancy between the calculated and experimental data.
Źródło:
International Journal of Electronics and Telecommunications; 2021, 67, 3; 529-534
2300-1933
Pojawia się w:
International Journal of Electronics and Telecommunications
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Application of fuzzy logic with genetic algorithms to FMEA method
Zastosowanie logiki rozmytej z algorytmami genetycznymi do metody FMEA
Autorzy:
Belu, N.
Ionescu, L.
Powiązania:
https://bibliotekanauki.pl/articles/203218.pdf
Data publikacji:
2014
Wydawca:
Politechnika Poznańska. Wydawnictwo Politechniki Poznańskiej
Tematy:
FMEA
fuzzy logic
genetic algorithm
Costs
Risk Priority Number
Opis:
Failure Mode and Effect Analysis (FMEA) is one of the well-known techniques of quality management that is used for continuous improvement in product or process design. One important issue of FMEA is the determination of the risk priorities of failure modes. The purpose of this paper is to compare three different methods for prioritizing failure modes in a process FMEA study. These methods are traditional approach, fuzzy logic and Genetic Algorithms using a risk-cost model of FMEA - to estimate the weight of risk factors. According to the findings, the integration of Genetic Algorithms and fuzzy revealed a difference in prioritizing failure modes among the methods. Because these methods eliminate some of the shortcomings of the traditional approach, they are useful tools in identifying the high priority failure modes. They can also provide the stability of process assurance.
Analiza przyczyn i skutków wad (FMEA) należy do dobrze znanych technik zarządzania jakością; jest wykorzystywana do ciągłego doskonalenia projektów, produktów lub procesów. Jedną z ważnych kwestii FMEA jest ustalanie priorytetów ryzyka niezgodności. Celem niniejszej pracy jest porównanie trzech metod ustalania poziomu ryzyka niezgodności: podejścia tradycyjnego, logiki rozmytej i algorytmów genetycznych na potrzeby analizy FMEA. Integracja algorytmów genetycznych i logiki rozmytej ujawniła różnicę w ustalaniu znaczenia przyczyn niezgodności. Ponieważ metody te eliminują niektóre wady podejścia tradycyjnego, są użytecznymi narzędziami w identyfikacji przyczyn niezgodności o wysokim ryzyku. Mogą również zapewniać stabilność procesu.
Źródło:
Zeszyty Naukowe Politechniki Poznańskiej. Organizacja i Zarządzanie; 2014, 62; 5-19
0239-9415
Pojawia się w:
Zeszyty Naukowe Politechniki Poznańskiej. Organizacja i Zarządzanie
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Genetic fuzzy approach to adaptive crane control system
Autorzy:
Smoczek, J.
Powiązania:
https://bibliotekanauki.pl/articles/243018.pdf
Data publikacji:
2012
Wydawca:
Instytut Techniczny Wojsk Lotniczych
Tematy:
anti-sway crane control
pole placement
fuzzy logic
genetic algorithm
Opis:
In automated manufacturing processes the safety, precise and fast transfer of goods realized by automated material handling devices is required to raise efficiency and productivity of manufacturing process. Hence, in those industrial branches where cranes are extensively used the problem of an anti-sway crane control is especially important to speed-up the time of transportation operations and ensures the safe and effective transportation operations. The precise positioning of a cargo requires controlling the speed of crane motion mechanisms to reduce the sway of a payload. Moreover, the anti-sway crane control scheme involves applying the adaptive techniques owing to the nonlinearities of a system that comes especially from stochastic variation of rope length on which a payload is suspended and mass of this payload. The paper provides the design method of an adaptive control system for a planar model of crane. The control system is based on the gain scheduling control scheme created using fuzzy logic controller with Takagi-Sugeno-Kang-type fuzzy implications. The design process of a gain scheduling control system consists in selecting such a suitable set of operating points at which the linear controllers are determined that interpolation control scheme ensures the expected control quality within the known range of nonlinear system parameters changes, when those parameters vary in relation to the exogenous variables: rope length and mass of a payload. The method that is proposed in this paper to solve the problem of designing the fuzzy gain scheduling crane control system for minimum set of operating points is based on the pole placement method and genetic algorithm.
Źródło:
Journal of KONES; 2012, 19, 4; 577-584
1231-4005
2354-0133
Pojawia się w:
Journal of KONES
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
The survey of soft computing techniques for reliability prediction
Autorzy:
Smoczek, J.
Powiązania:
https://bibliotekanauki.pl/articles/246835.pdf
Data publikacji:
2012
Wydawca:
Instytut Techniczny Wojsk Lotniczych
Tematy:
reliability prediction
artificial intelligence
fuzzy logic
artificial neural network
genetic algorithm
Opis:
The objective of reliability prediction is to estimate a time of upcoming nonoperational state at the current operational state of a system through real-time monitoring operational parameters and/or performances. Hence, the predictive (proactive) maintenance in industrial systems involves operational conditions monitoring and online forecasting the useful life of machines equipment to support the decision-making process in selection of the best maintenance action to be carried out. The advanced warning of the failure possibility can bring the attention of machines operators and maintenance personnel to impending danger, and facilitate planning preventive and corrective operations, as well as inventory managing. This problem has been extensively studied in many scientific works, where the predictive models are based on the data-driven approaches that can be generally divided into statistical techniques (regression, ARMA models, Bayesian probability distribution estimation, etc.), grey system theory, and soft computing methods. The artificial intelligence is frequently addressed to the predictive problem by utilizing the learning capability of artificial neural network (ANN), and possibility of nonlinear mapping using fuzzy rules-based system (FRBS) or recognizing and optimizing data-derived pattern by using evolutionary algorithms. The paper is a survey of intelligent methods for failure prediction, and delivers the review of examples of scientific works presenting the computational intelligence-based approaches to predictive problem.
Źródło:
Journal of KONES; 2012, 19, 3; 407-414
1231-4005
2354-0133
Pojawia się w:
Journal of KONES
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Evolutionary computing approaches to optimum design of fuzzy logic controller for a flexible robot system
Autorzy:
Subudhi, B.
Ranasingh, S.
Powiązania:
https://bibliotekanauki.pl/articles/230107.pdf
Data publikacji:
2013
Wydawca:
Polska Akademia Nauk. Czytelnia Czasopism PAN
Tematy:
flexible manipulator
fuzzy logic
genetic algorithm
bacteria foraging optimization
tip position tracking
Opis:
This paper presents the design of a Fuzzy Logic Controller (FLC) whose parameters are optimized by using Genetic Algorithm (GA) and Bacteria Foraging Optimization (BFO) for tip position control of a single link flexible manipulator. The proposed FLC is designed by minimizing the fitness function, which is defined as a function of tip position error, through GA and BFO optimization algorithms achieving perfect tip position tracking of the single link flexible manipulator. Then the tip position responses obtained by using both the above controllers are compared to suggest the best controller for the tip position tracking.
Źródło:
Archives of Control Sciences; 2013, 23, 4; 395-412
1230-2384
Pojawia się w:
Archives of Control Sciences
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Self-learning fuzzy predictor of exploitation system operating time
Autorzy:
Smoczek, J.
Szpytko, J.
Powiązania:
https://bibliotekanauki.pl/articles/247106.pdf
Data publikacji:
2011
Wydawca:
Instytut Techniczny Wojsk Lotniczych
Tematy:
operating time prediction
fuzzy logic
recursive least squares algorithm
overhead travelling crane
Opis:
The probability that a system is capable to operate satisfactorily significantly depends on reliability and maintainability of a system. The disadvantage of classic methods of system availability determining is that the probability of realizing by system tasks with expected quality depends on history of operational states and does not take into consideration actual operational conditions that have strong influence on risk-degree of down-time occurring, while the probability of degradation failure in exploitation system is a function of operating time and actual exploitation conditions. The problem of failures prediction can be solved by applying in diagnostics methods the intelligent computational algorithms. The intelligence computational methods enable to create the diagnosis tools that allow to formulate the prognosis of operating time of a system and predict of failure occurring based on the past and actual information about system's operational state. The paper presents the fuzzy logic approach to forecast the prognoses of the operating time of the exploitation system or its equipments according to the specified exploitation conditions that characterize the system exploitation state at the current time. The fuzzy system was based on the Takagi-Sugeno-Kang type fuzzy implications with singletons specifies in conclusions of rules. The fuzzy inference system input variables are the assumed parameters according to which the current exploitation state of the considered system can be evaluated.
Źródło:
Journal of KONES; 2011, 18, 4; 463-469
1231-4005
2354-0133
Pojawia się w:
Journal of KONES
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
FLC control for tuning exploration phase in bio-inspired metaheuristic
Autorzy:
Kiełkowicz, K.
Grela, D.
Powiązania:
https://bibliotekanauki.pl/articles/106299.pdf
Data publikacji:
2016
Wydawca:
Uniwersytet Marii Curie-Skłodowskiej. Wydawnictwo Uniwersytetu Marii Curie-Skłodowskiej
Tematy:
Bat algorithm
swarm intelligence
metaheuristics
optimization
fuzzy logic
Mamdami-Type inference system
Opis:
Growing popularity of the Bat Algorithm has encouraged researchers to focus their work on its further improvements. Most work has been done within the area of hybridization of Bat Algorithm with other metaheuristics or local search methods. Unfortunately, most of these modifications not only improves the quality of obtained solutions, but also increases the number of control parameters that are needed to be set in order to obtain solutions of expected quality. This makes such solutions quite impractical. What more, there is no clear indication what these parameters do in term of a search process. In this paper authors are trying to incorporate Mamdani type Fuzzy Logic Controller (FLC) to tackle some of these mentioned shortcomings by using the FLC to control the exploration phase of a bio-inspired metaheuristic. FLC also allows us to incorporate expert knowledge about the problem at hand and define expected behaviors of system – here process of searching in multidimensional search space by modeling the process of bats hunting for their prey.
Źródło:
Annales Universitatis Mariae Curie-Skłodowska. Sectio AI, Informatica; 2016, 16, 2; 32-38
1732-1360
2083-3628
Pojawia się w:
Annales Universitatis Mariae Curie-Skłodowska. Sectio AI, Informatica
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Technique Based on Fuzzy Logic for Cotton Bale Lay-down Management
Nowa technika zarządzania składowaniem bel bawełny
Autorzy:
Das, S.
Ghosh, A.
Powiązania:
https://bibliotekanauki.pl/articles/232810.pdf
Data publikacji:
2017
Wydawca:
Sieć Badawcza Łukasiewicz - Instytut Biopolimerów i Włókien Chemicznych
Tematy:
cotton bale
cluster analysis
fibre property
fuzzy logic
fuzzy c-means algorithm
składowanie bawełny
logika rozmyta
bela bawełny
Opis:
In this paper a new technique has been proposed for cotton bale management using fuzzy logic. The fuzzy c-means clustering algorithm has been applied for clustering cotton bales into 5 categories from 1200 randomly chosen bales of the J-34 variety. In order to cluster bales of different categories, eight fibre properties, viz., the strength, elongation, upper half mean length, length uniformity, short fibre content, micronaire, reflectance and yellowness of each bale have been considered. The fuzzy c-means clustering method is able to handle the haziness that may be present in the boundaries between adjacent classes of cotton bales as compared to the K-means clustering method. This method may be used as a convenient tool for the consistent picking of different bale mixes from any number of bales in a warehouse.
W artykule zaproponowano nową technikę zarządzania składowaniem bawełny opartą na logice rozmytej. Badaniu poddano 1200 losowo wybranych bel bawełny. W celu pogrupowania bel w 5 kategoriach zbadano właściwości, tj. wytrzymałość, wydłużenie, średnią długość, jednorodność długości, zawartość włókien krótkich, dojrzałość, współczynnik odbicia i zażółcenie każdej beli. Opracowana metoda może być stosowana jako wygodne narzędzie do sortowania różnych mieszanek z dowolnej liczby bel w magazynie.
Źródło:
Fibres & Textiles in Eastern Europe; 2017, 2 (122); 30-33
1230-3666
2300-7354
Pojawia się w:
Fibres & Textiles in Eastern Europe
Dostawca treści:
Biblioteka Nauki
Artykuł
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