1,721,271 research outputs found
Applied Mathematics and Computer Science
No full textThe International Journal of Applied Mathematics and Computer Science is a quarterly published jointly by the University of Zielona Góra and the Lubuskie Scientific Society in Zielona Góra, Poland, since 1991.
The journal strives to meet the demand for the presentation of interdisciplinary research in various fields related to control theory, applied mathematics, scientific computing and computer science. In particular, it publishes high quality original research results in the following areas:
•modern control theory and practice
•artificial intelligence methods and their applications
•applied mathematics and mathematical optimisation techniques
•mathematical methods in engineering, computer science, and biology.
We are primarily interested in presenting theoretical and application-oriented full-length research papers dealing with the following topics:
•control theory, including optimal control, system identification, adaptive and robust control, multivariable control, and non-linear systems
•dynamical systems, including spatiotemporal processes, control problems, state and parameter estimation, and sensor networks
•fault detection and diagnosis, including model-based approaches, observers, and classifiers
•fault-tolerant control, including the control of continuous-variable and quantised systems
•robotics, including modelling and simulation, mobile robots, and optimal trajectory planning
•mathematical modelling and simulation, including numerical algorithms
•optimisation, including mathematical optimisation techniques, global optimisation, and evolutionary algorithms
•classification and pattern recognition
•artificial intelligence, including neural networks, knowledge engineering, reasoning and learning models, expert and decision support systems, fuzzy systems, and search methods
•mathematical biology
•applications in engineering and medicine.
The editors welcome proposals for exchange between similar journals. Also, all persons interested in bringing out special issues of AMCS are encouraged to contact the Editor-in-Chief. Such issues may be published on any important and timely subject within the scope of the journal. All papers proposed for specials should be refereed and meet the same criteria for scientific quality as articles presented in regular issues.
The publication of AMCS is financially supported by the Polish Ministry of Science and Higher Education as well as the University of Zielona Góra
Artificial Intelligence Tools for Actuator Fault Diagnosis of an Unmanned Underwater Vehicle
No full textThe paper addresses the development of an artificial intelligence algorithm implemented for maximum power point tracking control of a unmanned underwater vehicle. It is shown that this algorithm tracks the optimum operation point and provides fast response even in the presence of faults. The strategy implements the tracking algorithm by using real—time measurements, while providing maximum power to the grid without using online data training. The solution is simulated in the Matlab and Simulink to verify the effectiveness of the proposed approach when fault–free and faulty conditions are considered. The simulation results highlight efficient, intrinsic and passive fault tolerant performances of the algorithm for general unmanned underwater vehicles with low inertia. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland A
Frequency Domain System Identification of Error–in–Variables Systems for Vibration–Based Monitoring
No full textData–Driven Wake Steering Control for a Simulated Wind Farm Model
No full textUpstream wind turbines yaw to divert their wakes away from downstream turbines, increasing the power produced. Nevertheless, the majority of wake steering techniques rely on offline lookup tables that translate a set of parameters, including wind speed and direction, to yaw angles for each turbine in a farm. These charts assume that every turbine is working well, however they may not be very accurate if one or more turbines are not producing their rated power due to low wind speed, malfunctions, scheduled maintenance, or emergency maintenance. This study provides an intelligent wake steering technique that, when calculating yaw angles, responds to the actual operating conditions of the turbine. A neural network is trained live to determine yaw angles from operating conditions, including turbine status, using a hybrid model and a learning-based method, i.e. an active control. The proposed control solution does not need to solve optimization problems for each combination of the turbines’ non-optimal working conditions in a farm; instead, the integration of learning strategy in the control design enables the creation of an active control scheme, in contrast to purely model-based approaches that use lookup tables provided by the wind turbine manufacturer or generated offline. The suggested methodology does not necessitate a substantial amount of training samples, unlike purely learning-based approaches like model-free reinforcement learning. In actuality, by taking use of the model during back propagation, the suggested approach learns more from each sample. Based on the flow redirection and induction in the steady state code, results are reported for both normal (nominal) wake steering with all turbines operating as well as defective conditions. It is a free tool for optimizing wind farms that The National Renewable Energy Laboratory (USA) offers. These yaw angles are contrasted and checked with those discovered through the resolution of an optimization issue. Active wake steering is made possible by the suggested solution, which employs a hybrid model and learning-based methodology, through sample efficient training and quick online evaluation. Finally, a hardware-in-the-loop test-bed is taken into consideration for assessing and confirming the performance of the suggested solutions in a more practical setting
Data-Driven and Model-Based Control Techniques for a Wind Turbine Benchmark Model
Full text linkAbstract: Wind turbine plants are complex dynamic and uncertain processes driven by stochastic inputs and disturbances, as well as different loads represented by gyroscopic, centrifugal, and gravitational forces. Moreover, as their aerodynamic models are nonlinear, both modelling and control become challenging problems. On one hand, high–fidelity simulators should contain different parameters and variables in order to accurately describe the main dynamic system behaviour. Therefore, the development of modelling and control for wind turbine systems should consider these complexity aspects. On the other hand, these control solutions have to include the main wind turbine dynamic characteristics without becoming too complicated. The main point of this paper is thus to provide two practical examples of development of robust control strategies when applied to a simulated wind turbine plant. Extended simulations with the wind turbine benchhmark model and the Monte–Carlo tool represent the instruments for assessing the robustness and reliability aspects of the developed control methodologies when the model–reality mismatch and measurement errors are also considered. Advantages and drawbacks of these regulation methods are also highlighted with respect to different control strategies via proper performance metrics
FUZZY MODELLING AND IDENTIFICATION FOR SUSTAINABLE CONTROL DESIGN OF AN OFFSHORE WIND FARM
No full textIn order to improve the safety, the reliability, and the efficiency of offshore wind park installations, thus
avoiding expensive unplanned maintenance, the accommodation of faults in their earlier occurrence is
fundamental. Therefore, the main contribution of this paper consists of the development of a fault
tolerant (the so-called ‘sustainable’) control scheme by means of a data-driven approach. In particular,
this strategy based on fuzzy model identification is exploited for deriving the mathematical description of
the required controller. Fuzzy theory is exploited here since it is able to approximate easily unknown
nonlinear systems and manage noisy measurements. Moreover, the fuzzy controller, which is directly
identified from the wind farm measurements, provides the straightforward achievement of the fault
tolerance feature. In general, an analytic approach, where the system nonlinearity is explicitly taken into
account, could require more complex design methodologies. This aspect of the work, followed by the
simpler solution relying on fuzzy rules, represents the key point when on-line implementations are
considered of the proposed control scheme. To highlight the potential of the proposed fault tolerant
control scheme in real applications, a Hardware–In–the–Loop test facility representing a realistic
offshore wind farm installation is considered to analyse the digital implementation of the designed
controller. The achieved results show that the developed scheme maintains desired performances, thus
validating its reliability and viability also in real-time implementations
Robust Control Applications to a Wind Turbine-Simulated System
No full textWind turbine plants are complex dynamic and uncertain processes driven by stochastic
inputs and disturbances, as well as different loads represented by gyroscopic, centrifugal
and gravitational forces. Moreover, as their aerodynamic models are nonlinear, both modelling
and control become challenging problems. On one hand, high-fidelity simulators
should contain different parameters and variables in order to accurately describe the main
dynamic system behaviour. Therefore, the development of modelling and control for
wind turbine systems should consider these complexity aspects. On the other hand, these
control solutions have to include the main wind turbine dynamic characteristics without
becoming too complicated. The main point of this chapter is thus to provide two practical
examples of development of robust control strategies when applied to a simulated wind
turbine plant. Experiments with the wind turbine simulator represent the instruments for
assessing the main aspects of the developed control methodologies
Robust Control Examples Applied to a Wind Turbine Simulated Model
Full text linkWind turbine plants are complex dynamic and uncertain processes driven by stochastic inputs and disturbances, as well as different loads represented by gyroscopic, centrifugal and gravitational forces. Moreover, as their aerodynamic models are nonlinear, both modeling and control become challenging problems. On the one hand, high-fidelity simulators should contain different parameters and variables in order to accurately describe the main dynamic system behavior. Therefore, the development of modeling and control for wind turbine systems should consider these complexity aspects. On the other hand, these control solutions have to include the main wind turbine dynamic characteristics without becoming too complicated. The main point of this paper is thus to provide two practical examples of the development of robust control strategies when applied to a simulated wind turbine plant. Extended simulations with the wind turbine benchmark model and the Monte Carlo tool represent the instruments for assessing the robustness and reliability aspects of the developed control methodologies when the model-reality mismatch and measurement errors are also considered. Advantages and drawbacks of these regulation methods are also highlighted with respect to different control strategies via proper performance metrics
Dynamic Neural Network Architecture Design for Predicting Remaining Useful Life of Dynamic Processes
No full textThe prediction of remaining useful life is critical in predictive health management. This is done to reduce the expenses associated with operation and maintenance by avoiding errors and failures in dynamic processes. Recently, the abilities of feature classification and automated extraction of neural networks in its convolutional forms have shown fascinating performance when used for estimating the remaining useful life of dynamic processes using deep learning structures. This was accomplished by putting these talents to the task of predicting how long the procedures would be beneficial. Existing network topologies, on the other hand, virtually entirely extract features at a single scale while neglecting important information at other sizes. Meanwhile, because of the architecture of a single network path, the comprehensiveness of the features discovered by these tools is limited. To address these concerns, the authors propose a network structure based on a feature fusion strategy on a parallel multiscale architecture. This structure is then utilized to compute the remaining useful life. This prototype is divided into two sections: the first is a multiscale feature extraction module designed to extract local information features, and the second is a causal convolution module designed to extract global information features by combining multi-layer causal convolution with average pooling. The multiscale feature extraction module is intended for the extraction of local information features, while the causal convolution module is intended for the extraction of global information features. Finally, the two distinct paths are joined to create a fully integrated layer. The simulations and results show that this method has the potential to improve the efficiency and accuracy of estimating the remaining useful life index. Furthermore, the advantages of the established strategy are shown by comparing the results obtained with those produced by applying cutting-edge techniques on a well-known data-set depicting a simulated turbofan engine
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