This paper presents an alternative method of obtaining linear mathematical models for the Buck-Boost converter based on the configuration in which it is used within a DC microgrid. This method is based on the state-sp...
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ISBN:
(数字)9798350351088
ISBN:
(纸本)9798350351095
This paper presents an alternative method of obtaining linear mathematical models for the Buck-Boost converter based on the configuration in which it is used within a DC microgrid. This method is based on the state-space averaging. The state-space averaged model of the Buck-Boost converter is non-linear and requires linearization around an operating point. State-space averaged models are quite accurate and provide much information about the behavior of the converter, but they lose accuracy through linearization. The method presented in this paper has the advantage of producing linear models without needing to linearize the models around an operating point. The steps through which the model is obtained are similar to those used in state-space averaging, with the addition of applying certain simplifying assumptions, leading to a simpler model. The simplified models obtained through this method have the advantage of preserving the essential characteristics of system dynamics.
In order to respond to a challenge, we set up a simple but efficient indoor positioning system, using off the shelf Wi-Fi access points (AP). The challenges of level positioning are overcome with a machine learning (M...
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The implementation of the MPTC scheme for high-speed or high-pole drives with high rated fundamental frequency poses many challenges. One of these is the low sampling to fundamental frequency ratio around the rated sp...
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Autonomous exploration of unknown environments using unmanned robots is a widely researched problem of our days. The focus of this paper is to provide a novel goal-selection method in response to the problems of the s...
Autonomous exploration of unknown environments using unmanned robots is a widely researched problem of our days. The focus of this paper is to provide a novel goal-selection method in response to the problems of the simple greedy goal selection algorithm. The presented method uses a tree structure built from the detected frontiers and selects the next goal of exploration using a depth-first search on the expanding tree. This prevents the exploring robot from leaving half-explored areas and backtracking to them later in the exploration. The method is tested in two simulated environments. The results are compared to those achieved using a simple greedy goal selection algorithm.
A permanent magnet synchronous motor's (PMSM) drive system depends heavily on the voltage source inverter (VSI). However, the VSI shows vulnerability to faults during operation, especially when it comes to open ci...
A permanent magnet synchronous motor's (PMSM) drive system depends heavily on the voltage source inverter (VSI). However, the VSI shows vulnerability to faults during operation, especially when it comes to open circuit switch faults, which are the most frequent and have the most significant negative impact on the drive system. In order to diagnose Open Circuit Faults (OCF) in the Voltage Source Inverter (VSI) of a three-phase permanent magnet synchronous motor drive system, this work employs the vector average current technique in Clark's transformation and an artificial neural network. Diagnostic variables are computed based on the current distortion to gather details about the fault. An artificial neural network (ANN) is put to use to process the fault information in order to determine and locate the specific location of the faulty switch. The study's simulation results show that the methodology employed effectively identifies and localises open circuit faults in a two-level, three-phase inverter.
The incorporation of real-time Hardware-In-the-Loop (HIL) simulators has become one of the pillars of the power electronics control design cycle. This integration is necessary to verify the effectiveness of controller...
The incorporation of real-time Hardware-In-the-Loop (HIL) simulators has become one of the pillars of the power electronics control design cycle. This integration is necessary to verify the effectiveness of controller implementations. Despite the rapid evolution in FPGA devices' computational capacity employed by Hardware-in-the-Loop (HIL) tools, developers are consistently faced with the need to strike a balance between acceptable accuracy and the practicality of implementing models and techniques. The methodology presented in this paper is for implementing a full-bridge converter using the Associate Discrete Circuit (ADC) model, specifically designed for real-time simulator applications. Furthermore, this study presents a novel methodology for parameter selection of (ADC) parameters utilizing the Artificial Bee Colony algorithm (ABC). The utilization of the ADC-based model facilitates the establishment of a uniform converter topology during simulation, irrespective of the switch states. The simulation results demonstrate the effectiveness of the introduced methodology in selecting optimal parameters for an ADC switch-based full-bridge converter, reducing overshoot in the converter's output voltage and current.
Model Predictive Control is a promising technique for electric drives, as it enables optimization for multiple parameters and offers reliable operation with non-linear systems. In this paper, a novel approach is used ...
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Vector magnetometers are widely used in mobile robot applications as compasses based on the Earth's magnetic field. These sensors are largely affected by objects that influence the magnetic field, which leads to u...
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ISBN:
(数字)9798350369250
ISBN:
(纸本)9798350369267
Vector magnetometers are widely used in mobile robot applications as compasses based on the Earth's magnetic field. These sensors are largely affected by objects that influence the magnetic field, which leads to unreliable measurements. In this paper, a new method is proposed for the compensation of disturbances caused by nearby objects. The method applies measurements of a technology for object detection and a magnetic sensor array, which are both installed on the mobile robot. The object detection sensor is utilized to provide the position of the detected objects in the robot's coordinate frame. The objects are assumed to have a dipole-like disturbance. The measurements of the magnetic sensor array are utilized to estimate the dipole parameters using genetic algorithm-based optimization. The compensated measurements can be computed by subtracting the estimated disturbances from the original measurements. The method is validated using simulations made with various setups based on different numbers of both magnetic sensors and detected objects. The obtained results show that the dipole parameters can be efficiently estimated if the number of sensors is higher than the number of objects. The method can also distinguish if a detected object does not influence the magnetic field. It is shown that the computed disturbances can be efficiently used for compensation.
Unmanned aerial vehicles (UAVs), also known as drones, are recently gaining increased research attention across various fields due to their flexibility and application potential. Drones have become increasingly popula...
Unmanned aerial vehicles (UAVs), also known as drones, are recently gaining increased research attention across various fields due to their flexibility and application potential. Drones have become increasingly popular in recent years due to their versatility and ability to perform tasks that were previously challenging or impossible. However, the full potential of drone technology has not been realized due to limitations in their communication and control systems. The integration of drone networks into a 5G environment has the potential to transform the capabilities of drones, providing high-speed, low-latency, and reliable communication and control capabilities. In this review paper, we provide an overview of the integration of drone networks into a 5G environment, discussing the technical aspects, potential applications, and challenges of this integration. We also examine the benefits of using 5G networks for drone operations, such as increased range, improved accuracy, and enhanced safety. Finally, we identify future research directions for the integration of drone networks into a 5G environment, highlighting the need for further research in areas such as drone security, energy efficiency, and spectrum management. Overall, this review paper provides a valuable resource for understanding the potential of drones in a 5G network and the challenges that must be addressed to fully realize this potential.
One aspect of structural health monitoring of bridges is to monitor vehicular traffic. The demand for moni-toring bridge performance and life cycle has led to measuring traffic flow. We have previously developed a dee...
One aspect of structural health monitoring of bridges is to monitor vehicular traffic. The demand for moni-toring bridge performance and life cycle has led to measuring traffic flow. We have previously developed a deep learning-based axle load estimator showing promising results considering COST 323 benchmark. This paper compares the deep learning-based solution to the established matrix method under different circumstances. Results show that the deep learning-based solution achieves better accuracy on several datasets of the BME-Simulated I corpus and has a better ability to handle noise than the matrix method.
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