The Control of Multiple Unmanned Aerial Vehicles

Nyhet

Häftad, Engelska, 2026

Av Mingyang Xie, Jiangbo Jia, Weizhen Wang

2 349 kr

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The Control of Multiple Unmanned Aerial Vehicles provides readers with a unified framework to address the challenges of multi-UAV target encirclement tracking control. This is achieved by comprehensively integrating the concepts of dynamic modelling, state and disturbance estimation, guidance law design, distributed control, obstacle avoidance and reinforcement learning-based formation tracking control with multi-UAV encirclement tracking systems. The book provides practical insight and solutions to the challenges of multi-UAV use, including limited sensing, communication constraint, wind disturbances and obstacle and collision avoidance, enabling readers to effectively apply the concepts to their work or research. Cutting-edge control techniques and advanced theories are explored, including robust distributed control, adaptive neural network approximation, reinforcement learning and multi-agent systems, presenting readers with state-of-the-art methodologies from which to develop practical applications of UAV formation. Each guidance and control method presented in the book is accompanied by a thorough comprehensive analysis to ensure that each is theoretically grounded. The book ensures transparency by providing open-access simulation codes, allowing readers to easily access and reproduce presented results.

The Control of Multiple Unmanned Aerial Vehicles (UAVs) fills a gap in the existing literature by providing solutions to the practical challenges of operating multiple unmanned aerial vehicles (UAVs)
Theoretical concepts are systematically integrated with practical solutions for meeting the challenges of guidance, control and obstacle avoidance in multi-UAV target tracking systems under different constraints
Readers are guided through the development of mathematical methodologies for state observer, guidance law design and obstacle avoidance problems associated with multi-UAV target encirclement tracking systems
Advanced methodologies for cooperative control of UAVs are described and open-access simulation codes are provided allowing readers to easily access and reproduce presented results and ensuring transparency

Produktinformation

Utgivningsdatum2026-01-01
Mått152 x 229 x undefined mm
Vikt450 g
FormatHäftad
SpråkEngelska
Antal sidor250
FörlagElsevier Science
ISBN9780443404337

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Teknik: allmänt inom Naturvetenskap och teknik

Dr Mingyang Xie is an Associate Professor at the College of Automation Engineering, Nanjing University of Aeronautics and Astronautics. He has been the Associate Editor of IET Electronics Letters SCI since 2022, the Junior Editor of Complex Engineering Systems since 2024, Guest Editor of the International Journal of Advanced Manufacturing Technology since 2019. He also serves as the workshop organizer of IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2019). Dr Jiangbo Jia is is based at the School of Electrical Engineering, Hebei University of Science and Technology in China. His research interests include multi-UAV guidance and nonlinear control. He also serves as a reviewer for several journals in the field of automation and control systems Weizhen Wang received the B.Eng. degree in Automation from Hebei University of Science and Technology, Shijiazhuang, China, 2017. He is currently pursuing a Ph.D. in control science and engineering at the Nanjing University of Aeronautics and Astronautics. His research interests include state estimation, wireless sensor network and multi-agent system

1. Abstract2. UAV Modeling3. Distributed Control of Target Cooperative Encirclement and Tracking Using Range‐Based Measurements4. Target Localization and Encirclement Control for Multi‐UAVs with Limited Information5. Distributed Robust Formation Tracking Control for Multi-UAV Systems6. Finite-Time Encirclement Control of Moving Target Tracking for UAV Formation7. Fixed-Time Encirclement Control of Moving Target Tracking for UAV Formation8. Predefined-Time Control for Multi-Targets Encirclement and Tracking for UAV Formation9. Reinforcement Learning-based Multi-UAVs Formation Tracking Control10. Conclusions and Future Directions