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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:
P1-TS fuzzy scheduling control system design using local pole placement and interval analysis
Autorzy:
Smoczek, J.
Powiązania:
https://bibliotekanauki.pl/articles/199816.pdf
Data publikacji:
2014
Wydawca:
Polska Akademia Nauk. Czytelnia Czasopism PAN
Tematy:
P1-TS fuzzy system
scheduling control
LPV discrete-time system
local pole placement
interval analysis
Opis:
The linear parameter-varying (LPV) discrete-time model based design of a fuzzy scheduling control scheme is developed through incorporating the advantages of P1-TS theory, and applying the local pole placement method and interval analysis of closed-loop system polynomial coefficients. The synthesis of fuzzy scheduling control scheme is proposed in the form of iterative procedure, which enables to find the appropriate number of intervals of a fuzzy interpolator ensuring that a family of local linear controllers places closed-loop polynomial coefficients within a desired range. The computational complexity of multidimensional fuzzy scheduling control scheme synthesis is reduced using a fundamental matrix method and recursive procedure for fuzzy rule-based interpretation. The usability of the proposed method is illustrated by an implementation example and experimental results obtained on a laboratory scaled overhead crane.
Źródło:
Bulletin of the Polish Academy of Sciences. Technical Sciences; 2014, 62, 3; 455-464
0239-7528
Pojawia się w:
Bulletin of the Polish Academy of Sciences. Technical Sciences
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Evolutionary optimization of interval mathematics-based design of a TSK fuzzy controller for anti-sway crane control
Autorzy:
Smoczek, J.
Powiązania:
https://bibliotekanauki.pl/articles/330615.pdf
Data publikacji:
2013
Wydawca:
Uniwersytet Zielonogórski. Oficyna Wydawnicza
Tematy:
interval mathematics
pole placement method
evolutionary algorithm
fuzzy logic
TSK controller
anti-sway crane control
matematyka przedziałowa
metoda lokowania biegunów
algorytm ewolucyjny
logika rozmyta
sterownik TSK
Opis:
A hybrid method combining an evolutionary search strategy, interval mathematics and pole assignment-based closed-loop control synthesis is proposed to design a robust TSK fuzzy controller. The design objective is to minimize the number of linear controllers associated with rule conclusions and tune the triangular-shaped membership function parameters of a fuzzy controller to satisfy stability and desired dynamic performances in the presence of system parameter variation. The robust performance objective function is derived based on an interval Diophantine equation. Thus, the objective of a fuzzy logic-based control scheme is to place all the closed-loop control system characteristic polynomial coefficients within desired intervals. The reproduction process in the proposed Evolutionary Algorithm (EA) is based on the arithmetical crossover, uniform and non-uniform mutation along with gene deletion/insertion mutation ensuring a diversity of genomes sizes, as well as a diversity in the parameter space of membership functions. The proposed algorithm was implemented to design a fuzzy logic-based anti-sway crane control system taking into consideration the rope length and the mass of a payload variation. The results of experiments conducted using the EA for different conditions assumed for system parameter intervals and desired closed-loop system performances are compared with results achieved using the iterative procedure which is also described in the paper.
Źródło:
International Journal of Applied Mathematics and Computer Science; 2013, 23, 4; 749-759
1641-876X
2083-8492
Pojawia się w:
International Journal of Applied Mathematics and Computer Science
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:
Interval arithmetic-based fuzzy discrete-time crane control scheme design
Autorzy:
Smoczek, J.
Powiązania:
https://bibliotekanauki.pl/articles/201270.pdf
Data publikacji:
2013
Wydawca:
Polska Akademia Nauk. Czytelnia Czasopism PAN
Tematy:
interval arithmetic
discrete-time control
fuzzy control
pole placement
anti-sway control
Opis:
In many manufacturing segments, container terminals and shipping yards the automation of material handling systems is an important element of enhancing productivity, safety and efficiency. The fast, precise and safe transfer of goods in crane operations requires a control application solving the problems, including non-collision trajectory planning and limitation of payload oscillations. The paper presents the interval arithmetic-based method of designing a discrete-time closed-loop anti-sway crane control system based on the fuzzy interpolation of linear controller parameters. The interval analysis of a closed-loop control system characteristic polynomial coefficients deviation from their nominal values is proposed to define a minimum number of fuzzy sets on the scheduling variables universe of discourse and to determine the distribution of triangular-shaped membership functions parameters, which satisfy the acceptable range of performances deterioration in the presence of the system’s parameters variation. The effectiveness of this method was proved in experiments conducted using the PAC system on the laboratory scaled overhead crane.
Źródło:
Bulletin of the Polish Academy of Sciences. Technical Sciences; 2013, 61, 4; 863-870
0239-7528
Pojawia się w:
Bulletin of the Polish Academy of Sciences. Technical Sciences
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Fuzzy logic and neural network approach to the indirect adaptive pole placement crane control system
Autorzy:
Smoczek, J.
Szpytko, J.
Powiązania:
https://bibliotekanauki.pl/articles/246396.pdf
Data publikacji:
2010
Wydawca:
Instytut Techniczny Wojsk Lotniczych
Tematy:
overhead crane
indirect adaptive
pole placement
fuzzy logic
neural network
Opis:
The problem under consideration in the paper of automation transportation operation realized by material handling devices is focused on time and accuracy of an overhead travelling crane's shifting process. The presented anti-sway crane control system was solved in the paper using combination of an indirect adaptive pole placement (IAPP) control method, fuzzy logic and artificial neural network. The presented approach to crane control is based on assuming structure of crane dynamic linear model with varying parameters, and linear closed-loop discrete control system consisting of proportional-derivative controllers with gains adjusted to changes of model's parameters using pole placement method (PPM). The parameters of crane dynamic model are estimated on-line using recursive least squares (RLS) algorithm. The estimation process is speeded up by neuro-fuzzy estimator, created using Takagi-Sugeno-Kang (TSK) fuzzy inference system, which determines the initial parameters of crane model based on scheduling variables, rope length and mass of a load changing in stochastic way. The neuro-fuzzy estimator is created in off-line process of neural network learning using least mean squares (LMS) method, based on a set of parametric output error models of crane dynamic identified for fixed values of rope length and mass of a load. The TSK estimator is next on-line improved by RLS algorithm.
Źródło:
Journal of KONES; 2010, 17, 2; 435-444
1231-4005
2354-0133
Pojawia się w:
Journal of KONES
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
A genetic fuzzy approach to estimate operation time of transport device
Autorzy:
Smoczek, J.
Szpytko, J.
Powiązania:
https://bibliotekanauki.pl/articles/247484.pdf
Data publikacji:
2011
Wydawca:
Instytut Techniczny Wojsk Lotniczych
Tematy:
failure prediction
fuzzy genetic system
material handling system
Opis:
The classic approach to evaluate the probability that an operational system is capable to operate satisfactorily and successfully perform the formulated tasks is based on availability that is coefficient which is determined based on the history of down-time and up-time occurring, while the risk-degree of down-time occurring strongly depends on the actual operational state of a system. 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, especially genetic fuzzy systems (GFSs) that combine fuzzy approximate reasoning and capability to learn and adaptation. The paper presents the fuzzy rule-based inference system used to predict the operating time of exploitation system according to the specified operational conditions. The proposed algorithm was used to design the fuzzy model applied to estimate the operating time of a system between the actual time and predicted time of the next failure occurring under the stated operational parameters. The fuzzy system allows to prognoses the time of the predicted failure based on the operational parameters which are used to evaluate the actual operational state of the system. The attention in the paper is focused on the evolutionary computational techniques applied to design the fuzzy inference system. The paper proposes the genetic algorithm based on the Pittsburgh method and real-valued chromosomes used to optimize the knowledge base and parameters of antecedents and conclusions of the Takagi-Sugeno-Kang (TSK) fuzzy implications. The paper is the contribution to the GFSs, which aim is to find an appropriate balance between accuracy and interpretability, and also contribution to the research field on the diagnosis methods based on soft computing techniques. The evolutionary algorithm was tested for designing the fuzzy operating time predictor of material handling device.
Źródło:
Journal of KONES; 2011, 18, 4; 601-608
1231-4005
2354-0133
Pojawia się w:
Journal of KONES
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Propeller optimization for small unmanned aerial vehicles
Autorzy:
Kusznir, T.
Smoczek, J.
Powiązania:
https://bibliotekanauki.pl/articles/246608.pdf
Data publikacji:
2017
Wydawca:
Instytut Techniczny Wojsk Lotniczych
Tematy:
unmanned aerial vehicles
particle swarm optimization (PSO)
airfoil modelling
Opis:
Small-unmanned aerial vehicle propellers usually have a low figure of merit due to operating in the low Reynold’s number region due to their size and velocity. The airflow on the airfoil becomes increasingly laminar in this region thus increasing the profile drag and consequently reducing the figure of merit of the rotor. In the article, the airfoil geometries are parameterized using the Class/Shape function transformation. Particle swarm optimization is used to design an airfoil, operating in a Reynolds number of 100,000, which has a high lift to drag ratio. To avoid exceeding geometric constraints of the airfoil, a deterministic box constraint is added to the algorithm. The optimized airfoil is then used for a preliminary design of a rotor; given some design, constraints on the tip chord the rotor radius and the blade root chord, with parameters that achieve the highest theoretical figure of merit. The rotor parameters are obtained using a combination of momentum theory and blade element theory. The figure of merit of an optimal propeller with the same geometric parameters is then compared using the optimized airfoil and the Clark Y airfoil. The optimization is done in MATLAB while the aerodynamic coefficients are obtained from XFOIL. The results of the numerical simulation are presented in the article.
Źródło:
Journal of KONES; 2017, 24, 2; 125-132
1231-4005
2354-0133
Pojawia się w:
Journal of KONES
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Fuzzy-Lyapunov based controller for a quadrocopter
Autorzy:
Kusznir, T.
Smoczek, J.
Powiązania:
https://bibliotekanauki.pl/articles/242206.pdf
Data publikacji:
2017
Wydawca:
Instytut Techniczny Wojsk Lotniczych
Tematy:
quadrocopter
fuzzy control
Lyapunov stability
Opis:
Quadrocopters are nonlinear and inherently unstable systems. To be able to account for the nonlinearities during more aggressive manoeuvres nonlinear control methods need to be utilized to obtain the desired position while at the same time guaranteeing stability. In the article, the quadrocopter dynamics is modelled using the Newton-Euler method. The propeller aerodynamics is modelled using a combination of momentum theory and blade element theory. There are two different control objectives; the 1st objective requires the quadrocopter to reach a desired attitude set point using, while the 2nd objective requires the quadrocopter to track an attitude trajectory. In both cases, Lyapunov stability criterion, in conjunction with LaSalle’s invariance principle, is used to guarantee the system becomes asymptotically stable. In the case of reaching the desired attitude set point, a direct Lyapunov control method is implemented with the control constants determined empirically. For the trajectory tracking, limited knowledge is assumed on the system dynamics and the Mamdani fuzzy controller is used with a rule base that satisfy the Lyapunov stability criterion. The fuzzy membership functions developed empirically and a centre of gravity defuzzification method is used. All simulations are done in MATLAB/Simulink. The results of the numerical simulation are presented in the article.
Źródło:
Journal of KONES; 2017, 24, 4; 125-132
1231-4005
2354-0133
Pojawia się w:
Journal of KONES
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Pole placement approach to discrete and neuro-fuzzy crane control system prototyping
Autorzy:
Smoczek, J.
Szpytko, J.
Powiązania:
https://bibliotekanauki.pl/articles/245378.pdf
Data publikacji:
2009
Wydawca:
Instytut Techniczny Wojsk Lotniczych
Tematy:
pole placement method
overhead travelling crane
neuro-fuzzy
Opis:
Today are observed rising requirements regarding increase productivity, reduced labour and maintenance cost, as well as optimizing the effectiveness of the material handling. The overhead travelling cranes play important role in selected manufacture applications. The paper presents methods of crane dynamic modelling and anti-sway discrete crane control system determining with using pole placement method (PPM). The TSK neuro-fuzzy crane controller was shown in the paper, as well as method of adaptation its control parameters to various values of rope length and masses of the load variables. The results of experiments carried out on real object were presented as well. Presented in the paper methods of crane dynamic modelling and control algorithm determining allow to prototype the effective anti-sway crane control systems. The method of determining conventional anti-sway crane control system based on discrete controllers type of PD elaborated with using pole placement method (PPM) was described in the paper. The TSK neuro-fuzzy crane controller was shown in the paper as well as method of adaptation its control parameters to various values ofrope length l and masses of the load m variables. The results of experiments carried out with using adaptive neuro-fuzzy TSK controller shown robustness on changeability of these variables and effectiveness of proposed control system.
Źródło:
Journal of KONES; 2009, 16, 4; 435-445
1231-4005
2354-0133
Pojawia się w:
Journal of KONES
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:
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:
Soft-constrained predictive control for an overhead crane
Autorzy:
Smoczek, J.
Szpytko, J.
Powiązania:
https://bibliotekanauki.pl/articles/242511.pdf
Data publikacji:
2017
Wydawca:
Instytut Techniczny Wojsk Lotniczych
Tematy:
overhead crane
predictive control
recursive least square estimation
particle swarm optimization (PSO)
Opis:
Reduction of transient and residual payload swing in crane systems is a key control objective to guarantee the safety and efficiency requirements. The fast and accurate payload positioning with swing suppression within the acceptable range to avoid accidents is the challenging problem due to the underactuated nature of crane systems. Since the actuated motion causes undesirable payload swing, the efficient control method should be developed to ensure fast and precise payload positioning and meet the safety requirements. The standard model predictive control method is not suitable for underactuated mechanical systems. In this article the two, soft and hard-constrained antisway predictive control strategies are compared in experiments carried out on a laboratory scaled overhead travelling crane. The both control schemes are developed based on the linear parameter-varying model of a planar crane system. The recursive least square algorithm with parameter projection is used to estimate the model parameters. The soft-constrained optimization problem is solved using the particle swarm optimization algorithm with the inertia weight linearly decreasing during iteration. The metaheuristic optimizer is applied to determine the sequence of optimal control increments subject to the hard constraint of the control input and soft constraint of the payload swing. The comparison of hard and soft-constrained predictive controllers is carried out on a laboratory stand for different payload deflection constraints.
Źródło:
Journal of KONES; 2017, 24, 3; 291-298
1231-4005
2354-0133
Pojawia się w:
Journal of KONES
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Robust fuzzy model predictive control of an overhead crane
Autorzy:
Smoczek, J.
Szpytko, J.
Powiązania:
https://bibliotekanauki.pl/articles/242944.pdf
Data publikacji:
2015
Wydawca:
Instytut Techniczny Wojsk Lotniczych
Tematy:
overhead crane
model predictive control
linear parameter varying model
fuzzy interpolation
Opis:
The method of controlling an overhead crane with respect to the variation of operating conditions and control constraints is developed using a model predictive control (MPC) and fuzzy interpolation applied in linear parameter varying (LPV) approach to crane dynamic modelling. The proposed control approach is based on the assumption that operating conditions vary within the known range of scheduling variables, and the parameters of a crane dynamic model can be interpolated by a quasi-linear fuzzy model designed through utilizing the P1-TS fuzzy theory. Hence, a crane dynamic is approximated through interpolation between a set of local linear models determined through identification experiments at the local operating points selected within the bounded intervals of scheduling variables. For the modelling assumptions, the control algorithm is developed based on a generalized predictive control (GPC) procedure taking into consideration the constraints on sway angle of a payload and control signal. Feasibility and applicability of the proposed control technique were confirmed during experiments carried out on a laboratory-scaled overhead crane. The results of experiments are presented and compared with performances of a fuzzy logic-based scheduling control scheme.
Źródło:
Journal of KONES; 2015, 22, 2; 205-212
1231-4005
2354-0133
Pojawia się w:
Journal of KONES
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Robust predictive control of an overhead crane
Autorzy:
Smoczek, J.
Szpytko, J.
Powiązania:
https://bibliotekanauki.pl/articles/243833.pdf
Data publikacji:
2017
Wydawca:
Instytut Techniczny Wojsk Lotniczych
Tematy:
overhead crane
predictive control
linear parameter varying model
recursive estimation
fuzzy interpolation
Opis:
The predictive control scheme is developed for an overhead crane using the generalized predictive procedure applied for the discrete time linear parameter-varying model of a crane dynamic. The robust control technique is developed with respect to the constraints of sway angle of a payload and control input signal. The two predictive strategies are presented and compared experimentally. In the first predictive control scheme, the online estimation of the parameters of a crane dynamic model is performed using the recursive least square algorithm. The second approach is a sensorless anti-sway control strategy. The sway angle feedback signal is estimated by a linear parameter-varying model of an unactuated pendulum system with the parameters interpolated using a quasi-linear fuzzy model designed through utilizing the P1-TS fuzzy theory. The fuzzy interpolator is applied to approximate the parameters of a crane discrete-time dynamic model within the range of scheduling variables changes: the rope length and mass of a payload. The experiments carried out on a laboratory scaled overhead crane confirmed effectiveness and feasibility of the proposed solutions. The implementation of control systems was performed using the PAC system with RX3i controller. The series of experiments carried out for different operating points proved robustness of the control approaches presented in the article.
Źródło:
Journal of KONES; 2017, 24, 2; 231-238
1231-4005
2354-0133
Pojawia się w:
Journal of KONES
Dostawca treści:
Biblioteka Nauki
Artykuł
Wyświetlanie 1-15 z 20

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