Neural Network Based UAV Optimal Control Algorithm for Energy Efficiency Maximization

Korneyev, Aleksandr; Gorobetz, Mikhail

doi:10.1109/rtucon51174.2020.9316556

Cited by 2 publications

(2 citation statements)

References 16 publications

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“…Also, for transport systems, we can assume several unmanned aerial vehicle (UAV) control systems. Here, CV and NN algorithms are used for several real-time detection and following functions [22], vehicle detection from UAV [23], or, for example, energy efficiency maximisation [24].…”

Section: Introductionmentioning

confidence: 99%

IoT-Enabled Single-Camera Speed Sensor for Smart City Tasks

Gorobetz,

Timofejevs,

Potapovs

et al. 2024

Electronics

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This article presents an innovative IoT-enabled single-camera speed sensor designed for smart city applications. The research encompasses the development of both hardware and software components, focusing on a computer vision and artificial neural network-based system. A novel aspect of this study is the implementation of a distance-measuring algorithm that eliminates the need for expensive LIDAR sensors traditionally used in speed cameras. Instead, the system relies on convolutional neural networks (CNN) and computer vision algorithms to estimate vehicle speed accurately. Field testing was conducted in real conditions over several months, generating sufficient data to assess the device’s ability to function under various adverse conditions. The results demonstrate the system’s capability to perform vehicle speed detection consistently across diverse conditions, showcasing its potential as a scalable solution for urban traffic management. This makes the proposed system not only more affordable but also simpler to deploy and maintain, thereby enhancing its suitability for widespread use in smart city environments.

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Section: Introductionmentioning

confidence: 99%

IoT-Enabled Single-Camera Speed Sensor for Smart City Tasks

Gorobetz,

Timofejevs,

Potapovs

et al. 2024

Electronics

Self Cite

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“…The voltage stabilizer powers the neural network device [26] and the control device. The neural network device implements the neural network [44] and the known neural network training algorithm (for example backpropagation algorithm) and also stores the training dataset. The developed algorithm of the control device includes the algorithm for minimum energy consumption search.…”

Section: Optimization Controllermentioning

confidence: 99%

Research and Development of Selflearning Neural Network Algorithm for Optimal Energy-efficient Autonomous Electrical Unmanned Vehicles Motion Control

Korņejevs

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The Thesis is devoted to the research and development of a method for optimizing the energy consumption of electric unmanned vehicles for optimal energy-efficient control. The control structure of unmanned vehicles is described. A mathematical model of vehicle movement, a mathematical model of a neural network, and a self-learning algorithm for optimal energy consumption using a neural network are described. As a result, a new method was developed for optimizing the energy consumption of an electric unmanned vehicle, providing optimal energy efficiency with changing vehicle parameters, without preliminary calculations and settings.

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Neural Network Based UAV Optimal Control Algorithm for Energy Efficiency Maximization

Cited by 2 publications

References 16 publications

IoT-Enabled Single-Camera Speed Sensor for Smart City Tasks

IoT-Enabled Single-Camera Speed Sensor for Smart City Tasks

Research and Development of Selflearning Neural Network Algorithm for Optimal Energy-efficient Autonomous Electrical Unmanned Vehicles Motion Control

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