Advanced Intelligent Decision-Making and Flight Control of Unmanned Aerial Vehicles

A special issue of Drones (ISSN 2504-446X). This special issue belongs to the section "Drone Communications".

Deadline for manuscript submissions: closed (30 September 2023) | Viewed by 17107

Special Issue Editors

College of Automation Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing 210016, China
Interests: intelligent fault diagnosis; fault tolerant control; helicopters; satellites; high-speed trains
Special Issues, Collections and Topics in MDPI journals
Department of Mechanical, Industrial and Aerospace Engineering, Concordia Institute of Aerospace Design and Innovation, Concordia University, 1455 de Maisonneuve Blvd. W., Montreal, QC H3G 1M8, Canada
Interests: guidance, navigation, and control; fault detection and diagnosis; fault-tolerant control; remote sensing with applications to unmanned aerial/space/ground/marine vehicles; smart grids; smart cities; cyber–physical systems
Special Issues, Collections and Topics in MDPI journals
School of Astronautics, Northwestern Polytechnical University, Xi’an 710129, China
Interests: intelligent decision-making; flight control; path planning
Special Issues, Collections and Topics in MDPI journals
College of Automation Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing 210016, China
Interests: intelligent control; flight control; path planning; discrete control
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues, 

With the deep integration of unmanned aerial vehicles (UAVs) with intelligent computing and other information technologies, an intelligent unmanned system has evolved, integrating information perception, autonomous decision-making and dynamic control. Such a system will gradually replace manned aircraft to perform military and civil tasks in the complex environments of the future. To successfully accomplish these tasks, UAVs are required not only to have intelligent decision-making abilities so as to adapt to unknown environments, but also to have good flight control performance in complex dynamic environments. Therefore, the intelligent decision-making technologies and the safe flight control schemes of UAVs in complex dynamic environments need to be fully considered in the future research into UAVs.

The journal focuses on the design and applications of drones, including research into control systems, artificial intelligence, mission planning and performance analysis, etc. This Special Issue on “Advanced Intelligent Decision-Making and Flight Control of Unmanned Aerial Vehicles” will cover a broad spectrum of topics related to advanced intelligent decision-making and flight control, focusing on new problems encountered in the research into UAVs.

This Special Issue aims to provide a forum for researchers and practitioners in the fields of decision-making and intelligent control on UAVs to disseminate their new ideas and research results. At the same time, the themes of “The 6th International Symposium on Autonomous Systems” are related to the Special Issue. Thus, we encourage the authors of outstanding original articles accepted for the conference to submit extended versions of their papers to this Special Issue. This Special Issue is also an open call for other high-quality papers in this research field, though the authors who wish to contribute directly to the Special Issue are in principle required to register for the conference and present their published work at the conference. Topics of interest for research papers include, but are not limited to, the following:

  • Artificial Intelligence
  • Unmanned System Command and Control
  • Guidance Law Design
  • Active Vibration Control                   
  • Neural/Fuzzy Control
  • Fault-Tolerant Control                      
  • Discrete Controller Design
  • Novel Disturbance Rejection Control        
  • Adaptive Estimation and Control
  • Path Planning                            
  • Cooperative Control of UAVs
  • UAV Modeling and Simulation
  • Decision-Making
  • Task Assignment
  • Health Management

Prof. Dr. Mou Chen
Prof. Dr. Bin Jiang
Prof. Dr. Youmin Zhang
Dr. Zixuan Zheng
Dr. Shuyi Shao
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Drones is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • intelligent decision-making
  • intelligent control
  • learning control
  • discrete control
  • fault-tolerant control
  • position/orientation control
  • disturbance observer
  • cooperative control
  • neural networks
  • adaptive control

Related Special Issue

Published Papers (10 papers)

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Research

17 pages, 4864 KiB  
Article
PPO-Based Attitude Controller Design for a Tilt Rotor UAV in Transition Process
by Rui Yang, Changping Du, Yao Zheng, Huzhen Gao, Yuean Wu and Tianrui Fang
Drones 2023, 7(8), 499; https://doi.org/10.3390/drones7080499 - 31 Jul 2023
Cited by 1 | Viewed by 1066
Abstract
The complex aerodynamic changes of the tilt-rotor UAV (TRUAV) in the transition process show strong nonlinearity, which brings a great impact on the stability of the vehicle attitude. This study aims to design a PPO-based RL controller for attitude control in the transition [...] Read more.
The complex aerodynamic changes of the tilt-rotor UAV (TRUAV) in the transition process show strong nonlinearity, which brings a great impact on the stability of the vehicle attitude. This study aims to design a PPO-based RL controller for attitude control in the transition process. A reinforcement-learning PPO approach is used to learn the control strategy by interacting directly with the environment. And the reward function is designed and improved for the transition process. The performance of the proposed controller is tested and compared by simulation. The results show that the PPO algorithm is more suitable for the tilt-rotor transition process control than the A2C algorithm. Our proposed reward function improves the attitude control performance and the designed RL controller has good adaptability to changes in the takeoff weight, the diagonal wheelbase and the tilt rate. This study highlights the effectiveness and potential of reinforcement learning for tilt-rotor UAV transition process attitude control. These findings contribute to the advancement of autonomous flight systems by providing insights into the application of reinforcement learning algorithms. These results have important implications for the development of intelligent flight control systems and could guide future research in this area. Full article
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27 pages, 4913 KiB  
Article
Multi-UAV Roundup Inspired by Hierarchical Cognition Consistency Learning Based on an Interaction Mechanism
by Longting Jiang, Ruixuan Wei and Dong Wang
Drones 2023, 7(7), 462; https://doi.org/10.3390/drones7070462 - 11 Jul 2023
Viewed by 861
Abstract
This paper is concerned with the problem of multi-UAV roundup inspired by hierarchical cognition consistency learning based on an interaction mechanism. First, a dynamic communication model is constructed to address the interactions among multiple agents. This model includes a simplification of the communication [...] Read more.
This paper is concerned with the problem of multi-UAV roundup inspired by hierarchical cognition consistency learning based on an interaction mechanism. First, a dynamic communication model is constructed to address the interactions among multiple agents. This model includes a simplification of the communication graph relationships and a quantification of information efficiency. Then, a hierarchical cognition consistency learning method is proposed to improve the efficiency and success rate of roundup. At the same time, an opponent graph reasoning network is proposed to address the prediction of targets. Compared with existing multi-agent reinforcement learning (MARL) methods, the method developed in this paper possesses the distinctive feature that target assignment and target prediction are carried out simultaneously. Finally, to verify the effectiveness of the proposed method, we present extensive experiments conducted in the scenario of multi-target roundup. The experimental results show that the proposed architecture outperforms the conventional approach with respect to the roundup success rate and verify the validity of the proposed model. Full article
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21 pages, 1043 KiB  
Article
Task Assignment of UAV Swarms Based on Deep Reinforcement Learning
by Bo Liu, Shulei Wang, Qinghua Li, Xinyang Zhao, Yunqing Pan and Changhong Wang
Drones 2023, 7(5), 297; https://doi.org/10.3390/drones7050297 - 29 Apr 2023
Cited by 3 | Viewed by 2266
Abstract
UAV swarm applications are critical for the future, and their mission-planning and decision-making capabilities have a direct impact on their performance. However, creating a dynamic and scalable assignment algorithm that can be applied to various groups and tasks is a significant challenge. To [...] Read more.
UAV swarm applications are critical for the future, and their mission-planning and decision-making capabilities have a direct impact on their performance. However, creating a dynamic and scalable assignment algorithm that can be applied to various groups and tasks is a significant challenge. To address this issue, we propose the Extensible Multi-Agent Deep Deterministic Policy Gradient (Ex-MADDPG) algorithm, which builds on the MADDPG framework. The Ex-MADDPG algorithm improves the robustness and scalability of the assignment algorithm by incorporating local communication, mean simulation observation, a synchronous parameter-training mechanism, and a scalable multiple-decision mechanism. Our approach has been validated for effectiveness and scalability through both simulation experiments in the Multi-Agent Particle Environment (MPE) and a real-world experiment. Overall, our results demonstrate that the Ex-MADDPG algorithm is effective in handling various groups and tasks and can scale well as the swarm size increases. Therefore, our algorithm holds great promise for mission planning and decision-making in UAV swarm applications. Full article
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20 pages, 2417 KiB  
Article
Path-Following Control of Small Fixed-Wing UAVs under Wind Disturbance
by Pengyun Chen, Guobing Zhang, Jiacheng Li, Ze Chang and Qichen Yan
Drones 2023, 7(4), 253; https://doi.org/10.3390/drones7040253 - 07 Apr 2023
Cited by 2 | Viewed by 2194
Abstract
Aiming at the problems of low following accuracy and weak anti-disturbance ability in the three-dimensional path-following control of small fixed-wing Unmanned Aerial Vehicles (UAV), a Globally Stable Integral Sliding Mode Radial Basis Function S-Plane (GSISM+RBF S-Plane) controller is designed. The controller adopts the [...] Read more.
Aiming at the problems of low following accuracy and weak anti-disturbance ability in the three-dimensional path-following control of small fixed-wing Unmanned Aerial Vehicles (UAV), a Globally Stable Integral Sliding Mode Radial Basis Function S-Plane (GSISM+RBF S-Plane) controller is designed. The controller adopts the inner and outer loop mode, the outer loop adopts the Globally Stable Integral Sliding Mode (GSISM) control, and the inner loop adopts the S-Plane control. At the same time, the unknown disturbance in the model is estimated via an RBF neural network. Firstly, the outer loop controller is designed based on the GSISM, and its stability is proved using the Lyapunov theory. Then, the S-Plane controller is designed for the instruction signal of the inner loop. Considering the complexity of the derivation in the S-Plane controller, a second-order differentiator is introduced. Finally, considering the problem of external wind disturbance, the controller is modeled, studied, and processed in order to better reflect the impact of real external wind on UAV path following. Finally, the Globally Stable Sliding Mode (GSSM) control and Globally Stable Integral Sliding Mode S-Plane (GSISM S-Plane) control are used for a comparative experiment. The simulation results show that the designed GSISM+RBF S-Plane controller can accurately track the ideal path compared with the GSSM and GSISM S-Plane controller, and it has good control performance and anti-disturbance performance. Full article
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17 pages, 1714 KiB  
Article
Autonomous Maneuver Decision-Making of UCAV with Incomplete Information in Human-Computer Gaming
by Shouyi Li, Qingxian Wu, Bin Du, Yuhui Wang and Mou Chen
Drones 2023, 7(3), 157; https://doi.org/10.3390/drones7030157 - 23 Feb 2023
Cited by 2 | Viewed by 1331
Abstract
In human-computer gaming scenarios, the autonomous decision-making problem of an unmanned combat air vehicle (UCAV) is a complex sequential decision-making problem involving multiple decision-makers. In this paper, an autonomous maneuver decision-making method for UCAV that considers the partially observable states of Human (the [...] Read more.
In human-computer gaming scenarios, the autonomous decision-making problem of an unmanned combat air vehicle (UCAV) is a complex sequential decision-making problem involving multiple decision-makers. In this paper, an autonomous maneuver decision-making method for UCAV that considers the partially observable states of Human (the adversary) is proposed, building on a game-theoretic approach. The maneuver decision-making process within the current time horizon is modeled as a game of Human and UCAV, which significantly reduces the computational complexity of the entire decision-making process. In each established game decision-making model, an improved maneuver library that contains all possible maneuvers (called the continuous maneuver library) is designed, and each of these maneuvers corresponds to a mixed strategy of the established game. In addition, the unobservable states of Human are predicted via the Nash equilibrium strategy of the previous decision-making stage. Finally, the effectiveness of the proposed method is verified by some adversarial experiments. Full article
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17 pages, 6205 KiB  
Article
Attitude Fault-Tolerant Control of Aerial Robots with Sensor Faults and Disturbances
by Ngoc-P. Nguyen and Phongsaen Pitakwatchara
Drones 2023, 7(3), 156; https://doi.org/10.3390/drones7030156 - 23 Feb 2023
Cited by 1 | Viewed by 1387
Abstract
In this paper, sensor fault diagnosis and fault tolerant control strategy are investigated for quadcopters under sensor faults and disturbances. We propose the fault diagnosis estimation system and the fault-tolerant control (FTC) method. The fault diagnosis system provides time-varying sensor fault estimation under [...] Read more.
In this paper, sensor fault diagnosis and fault tolerant control strategy are investigated for quadcopters under sensor faults and disturbances. We propose the fault diagnosis estimation system and the fault-tolerant control (FTC) method. The fault diagnosis system provides time-varying sensor fault estimation under an unknown bound of disturbances. Moreover, the fault-tolerant control eliminates disturbance that is estimated through the associated disturbance observer. Overall, the proposed FTC guarantees the finite-time tracking convergence using nonsingular fast terminal sliding mode algorithm. Stability of the closed-loop system is validated through the Lyapunov theory. Finally, conventional nonsingular fast terminal sliding mode and adaptive neural network sliding mode control are compared with the proposed method through simulations under sensor faults and disturbances with different scenarios. Full article
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20 pages, 1197 KiB  
Article
Integral Backstepping Sliding Mode Control for Unmanned Autonomous Helicopters Based on Neural Networks
by Min Wan, Mou Chen and Mihai Lungu
Drones 2023, 7(3), 154; https://doi.org/10.3390/drones7030154 - 22 Feb 2023
Cited by 2 | Viewed by 1313
Abstract
In this paper, we propose an adaptive control approach to deal with the problems of input saturation, external disturbances, and uncertainty in the unmanned autonomous helicopter system. The dynamics of the system take into account the presence of input saturation, uncertainty, and external [...] Read more.
In this paper, we propose an adaptive control approach to deal with the problems of input saturation, external disturbances, and uncertainty in the unmanned autonomous helicopter system. The dynamics of the system take into account the presence of input saturation, uncertainty, and external disturbances. Auxiliary systems are built to handle the input saturation. The neural networks are applied to approximate the uncertain terms. The control scheme combining integral backstepping and sliding mode control is developed in position and attitude subsystems, respectively. In the closed-loop system, the boundedness of the signals is proved by means of the Lyapunov theory. The simulation demonstrates that the approach has good robustness and tracking performance. Full article
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18 pages, 2089 KiB  
Article
Adaptive Fault-Tolerant Tracking Control of Quadrotor UAVs against Uncertainties of Inertial Matrices and State Constraints
by Shuai Yang, Zhihui Zou, Yingchao Li, Haodong Shi and Qiang Fu
Drones 2023, 7(2), 107; https://doi.org/10.3390/drones7020107 - 04 Feb 2023
Cited by 1 | Viewed by 1638
Abstract
This paper presents a study on a quadrotor unmanned aerial vehicle (UAV) fault-tolerant control scheme. According to the attitude model and safety control of the aircraft under the uncertainty of inertial matrix, the attitude state constraint by reinforcement learning is designed to ensure [...] Read more.
This paper presents a study on a quadrotor unmanned aerial vehicle (UAV) fault-tolerant control scheme. According to the attitude model and safety control of the aircraft under the uncertainty of inertial matrix, the attitude state constraint by reinforcement learning is designed to ensure safety. Even if the boundary is crossed, it can be pulled back to the boundary by means of a designed penalty function with reinforcement learning. Meanwhile, in order to inhibit the oscillation caused by immediate reward as usual, an adaptive update law is proposed. Furthermore, considering the coupled actuator fault and system input saturation due to uncertainty of inertial matrix, the Nussbaum-type function is utilized in this work to handle this challenge, which likely causes the singularity of inertia matrix. As a consequence, combined with the Lyapunov stability theory, it is confirmed that the proposed FTC scheme ensures that all the closed-loop signals are bounded. Simulation results are carried out to illustrate the effectiveness and advantage of the proposed control scheme. Full article
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21 pages, 17125 KiB  
Article
Disturbance Interval Observer-Based Robust Constrained Control for Unmanned Aerial Vehicle Path Following
by Yaping Song, Kenan Yong and Xiaolong Wang
Drones 2023, 7(2), 90; https://doi.org/10.3390/drones7020090 - 27 Jan 2023
Cited by 5 | Viewed by 1682
Abstract
This work presents a robust constrained path-following control scheme for the unmanned aerial vehicle (UAV) under wind disturbances. Through appointing the projection from the UAV to the path, the Serret–Frenet frame is introduced to reduce the complexity of the path-following problem. Specifically, the [...] Read more.
This work presents a robust constrained path-following control scheme for the unmanned aerial vehicle (UAV) under wind disturbances. Through appointing the projection from the UAV to the path, the Serret–Frenet frame is introduced to reduce the complexity of the path-following problem. Specifically, the disturbance interval observer is employed to generate the interval of the wind disturbances. Then, the path-following control design is presented based on the dynamic surface control technique, and the auxiliary system is adopted to deal with the command limitation during the design process. Accordingly, the stability of the closed-loop system is analyzed. The effectiveness of the developed control scheme is demonstrated using numerical simulations. Full article
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19 pages, 5783 KiB  
Article
Formation Tracking Control for Multi-Agent Systems with Collision Avoidance and Connectivity Maintenance
by Yitao Qiao, Xuxing Huang, Bin Yang, Feilong Geng, Bingheng Wang, Mingrui Hao and Shuang Li
Drones 2022, 6(12), 419; https://doi.org/10.3390/drones6120419 - 15 Dec 2022
Cited by 3 | Viewed by 1490
Abstract
This paper investigates the formation tracking control of multiple agents with a double-integrator model and presents a novel distributed control framework composed of three items: a potential-based gradient term, a formation term, and a navigation term. Considering the practical situation, each agent is [...] Read more.
This paper investigates the formation tracking control of multiple agents with a double-integrator model and presents a novel distributed control framework composed of three items: a potential-based gradient term, a formation term, and a navigation term. Considering the practical situation, each agent is regarded as a rigid-body with a safe radius and a sensing region. To enable collision avoidance and connectivity maintenance among multiple agents, a new potential function with fewer parameters is established. The predetermined formation is also achieved by taking the difference between the actual displacement and the desired displacement as a consensus variable. Lastly, the virtual navigator provides trajectory signals and guides the multiple agent movement. Two instances of an equilateral triangle formation and a hexagonal formation are used in the simulation to verify the proposed method. Full article
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