The Control of Multiple Unmanned Aerial Vehicles
- 1st Edition - January 1, 2026
- Authors: Mingyang Xie, Jiangbo Jia, Weizhen Wang
- Language: English
- Paperback ISBN:9 7 8 - 0 - 4 4 3 - 4 0 4 3 3 - 7
- eBook ISBN:9 7 8 - 0 - 4 4 3 - 4 0 4 3 4 - 4
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 ach… Read more
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
Researchers and academics in the fields of UAV flight control theory, multi-agent systems, robotics, aerospace, mechatronics, aeronautical and mechanical engineering. Industry professionals and practitioners involved in aeronautical engineering, flight control technology, multi-UAV formation control, distributed control for multi-agent systems
1. Abstract
1.1. Background and Motivations
1.2. Introduction to UAV and its applications
1.3. Introduction to Cooperative Control of Multi-UAVs
1.4. Introduction to Cooperative Target Tracking Control of Multi-UAVs
1.5. Summary of Chapter 1
2. UAV Modeling
2.1. Coordinate System Definition
2.2. Kinematics model and Dynamics Model
2.3. Consensus Theory
2.4. Formation Communication Modeling
2.5. Relevant Lemmas
2.6.. Summary of Chapter 2
3. Distributed Control of Target Cooperative Encirclement and Tracking Using Range‐Based Measurements
3.1. Distributed observer design
3.2. Guidance law design
3.3. Main Results
3.4. Summary of Chapter 3
4. Target Localization and Encirclement Control for Multi‐UAVs with Limited Information
4.1. Problem Formulation
4.2. Target relative position estimator design
4.3. Moving path following controller design
4.4. Cooperative target pursuit
4.5. Main Results
4.6. Summary of Chapter 4
5. Distributed Robust Formation Tracking Control for Multi-UAV Systems
5.1. Problem formulation
5.2. Finite-time disturbance observer design
5.3. Distributed robust formation controller design
5.4. Main Results
5.5. Summary of Chapter 5
6. Finite-Time Encirclement Control of Moving Target Tracking for UAV Formation
6.1. Preliminaries
6.2. Guidance law design for known wind condition
6.3. Guidance law design for unknown wind condition
6.4. Main Results
6.5. Summary of Chapter 6
7. Fixed-Time Encirclement Control of Moving Target Tracking for UAV Formation
7.1. Preliminaries
7.2. Fixed-time interconnected systems theorem
7.3. RBF-NN adaptive disturbance observer
7.4. Design of dynamic event-triggered control law
7.5. Main Results
7.6. Summary of Chapter 7
8. Predefined-Time Control for Multi-Targets Encirclement and Tracking for UAV Formation
8.1. Multi-objective clustering and multi-UAVs task assignment
8.2. Adaptive predefined-time extended state observer
8.3. Elliptical orbital tracking guidance law design
8.4. Main Results
8.5. Summary of Chapter 8
9. Reinforcement Learning-based Multi-UAVs Formation Tracking Control
9.1. Preliminaries and problem definition
9.2. Adaptive integral sliding mode controller design
9.3. Reinforcement learning-based optimal tracking controller design
9.4. Main Results
9.5. Summary of Chapter 9
10. Conclusions and Future Directions
10.1. Conclusions
10.2. Future directions
1.1. Background and Motivations
1.2. Introduction to UAV and its applications
1.3. Introduction to Cooperative Control of Multi-UAVs
1.4. Introduction to Cooperative Target Tracking Control of Multi-UAVs
1.5. Summary of Chapter 1
2. UAV Modeling
2.1. Coordinate System Definition
2.2. Kinematics model and Dynamics Model
2.3. Consensus Theory
2.4. Formation Communication Modeling
2.5. Relevant Lemmas
2.6.. Summary of Chapter 2
3. Distributed Control of Target Cooperative Encirclement and Tracking Using Range‐Based Measurements
3.1. Distributed observer design
3.2. Guidance law design
3.3. Main Results
3.4. Summary of Chapter 3
4. Target Localization and Encirclement Control for Multi‐UAVs with Limited Information
4.1. Problem Formulation
4.2. Target relative position estimator design
4.3. Moving path following controller design
4.4. Cooperative target pursuit
4.5. Main Results
4.6. Summary of Chapter 4
5. Distributed Robust Formation Tracking Control for Multi-UAV Systems
5.1. Problem formulation
5.2. Finite-time disturbance observer design
5.3. Distributed robust formation controller design
5.4. Main Results
5.5. Summary of Chapter 5
6. Finite-Time Encirclement Control of Moving Target Tracking for UAV Formation
6.1. Preliminaries
6.2. Guidance law design for known wind condition
6.3. Guidance law design for unknown wind condition
6.4. Main Results
6.5. Summary of Chapter 6
7. Fixed-Time Encirclement Control of Moving Target Tracking for UAV Formation
7.1. Preliminaries
7.2. Fixed-time interconnected systems theorem
7.3. RBF-NN adaptive disturbance observer
7.4. Design of dynamic event-triggered control law
7.5. Main Results
7.6. Summary of Chapter 7
8. Predefined-Time Control for Multi-Targets Encirclement and Tracking for UAV Formation
8.1. Multi-objective clustering and multi-UAVs task assignment
8.2. Adaptive predefined-time extended state observer
8.3. Elliptical orbital tracking guidance law design
8.4. Main Results
8.5. Summary of Chapter 8
9. Reinforcement Learning-based Multi-UAVs Formation Tracking Control
9.1. Preliminaries and problem definition
9.2. Adaptive integral sliding mode controller design
9.3. Reinforcement learning-based optimal tracking controller design
9.4. Main Results
9.5. Summary of Chapter 9
10. Conclusions and Future Directions
10.1. Conclusions
10.2. Future directions
- Edition: 1
- Published: January 1, 2026
- Language: English
MX
Mingyang Xie
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).
Affiliations and expertise
Associate Professor, College of Automation Engineering, Nanjing University of Aeronautics and Astronautics, ChinaJJ
Jiangbo Jia
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
Affiliations and expertise
School of Electrical Engineering, Hebei University of Science and Technology, ChinaWW
Weizhen Wang
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
Affiliations and expertise
Nanjing University of Aeronautics and Astronautics, China