Typical Fault Estimation and Dynamic Analysis of a Leader-Follower Unmanned Aerial Vehicle Formation

Shen, Jian; Zhu, Qingyu; Wang, Xiaoguang; Chen, Pengyun

doi:10.1155/2021/6656422

Cited by 4 publications

(1 citation statement)

References 39 publications

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“…A simplified model is adopted in the formation control problem. Considering that the motion of the formation is a first-order system, the motions of the leader and the follower are independent of each other [29]. The leader's motion equations are shown as…”

Section: Environment and Mdp Frameworkmentioning

confidence: 99%

A Deep $Q$ -Network-Based Collaborative Control Research for Smart Ammunition Formation

Shen

Zhang

Zhu

et al. 2022

International Journal of Aerospace Engineering

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The smart ammunition formation (SAF) system model usually has the characteristics of complexity, time variation, and nonlinearity. With the consideration of random factors, such as sensor error and environmental disturbance, the system model cannot be modeled accurately. To deal with this problem, this paper investigated an intelligent deep Q -network- (DQN-) based control algorithm for the SAF collaborative control, which deals with the high dynamics and uncertainty in the SAF flight environment. In the environment description of the SAF, we built a dynamic model to represent the system joint states, which referred to the smart ammunition’s velocity, the trajectory inclination angle, the ballistic deflection angle, and the relative position between different formation nodes. Next, we describe the SAF collaborative control process as a Markov decision process (MDP) with the application of the reinforcement learning (RL) technique. Then, the basic framework ε -imitation action-selecting strategy and the algorithm details were developed to address the SAF control problem based on the DQN scheme. Finally, the numerical simulation was carried out to verify the effectiveness and portability of the DQN-based algorithm. The average total reward curve showed a reasonable convergence, and the relative kinematic relationship among the formation nodes met the requirements of the controller design. It illustrated that the DQN-based algorithm obtained a novel performance in the SAF collaborative control.

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Section: Environment and Mdp Frameworkmentioning

confidence: 99%

A Deep $Q$ -Network-Based Collaborative Control Research for Smart Ammunition Formation

Shen

Zhang

Zhu

et al. 2022

International Journal of Aerospace Engineering

Self Cite

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Leader–follower UAVs formation control based on a deep Q-network collaborative framework

Liu,

Li,

Shen

et al. 2024

Sci Rep

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This study examines a collaborative framework that utilizes an intelligent deep Q-network to regulate the formation of leader–follower Unmanned Aerial Vehicles (UAVs). The aim is to tackle the challenges posed by the highly dynamic and uncertain flight environment of UAVs. In the context of UAVs, we have developed a dynamic model that captures the collective state of the system. This model encompasses variables like as the relative positions, heading angle, rolling angle, and velocity of different nodes in the formation. In the subsequent section, we elucidate the operational procedure of UAVs in a collaborative manner, employing the conceptual framework of Markov Decision Process (MDP). Furthermore, we employ the Reinforcement Learning (RL) to facilitate this process. In light of this premise, a fundamental framework is presented for addressing the control problem of UAVs utilizing the DQN scheme. This framework encompasses a technique for action selection known as $$\varepsilon$$ ε -imitation, as well as algorithmic specifics. Finally, the efficacy and portability of the DQN-based approach are substantiated by numerical simulation validation. The average reward curve demonstrates a satisfactory level of convergence, and kinematic link between the nodes inside the formation satisfies the essential requirements for the creation of a controller.

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Design of intelligent transportation verification platform based on hardware in the loop simulation technology

Guo

Dong

et al. 2022

2022 8th International Conference on Hydraulic and Civil Engineering: Deep Space Intelligent Development and Utilization Forum

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Typical Fault Estimation and Dynamic Analysis of a Leader-Follower Unmanned Aerial Vehicle Formation

Cited by 4 publications

References 39 publications

A Deep $Q$ -Network-Based Collaborative Control Research for Smart Ammunition Formation

A Deep $Q$ -Network-Based Collaborative Control Research for Smart Ammunition Formation

Leader–follower UAVs formation control based on a deep Q-network collaborative framework

Design of intelligent transportation verification platform based on hardware in the loop simulation technology

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Typical Fault Estimation and Dynamic Analysis of a Leader-Follower Unmanned Aerial Vehicle Formation

Cited by 4 publications

References 39 publications

A Deep Q -Network-Based Collaborative Control Research for Smart Ammunition Formation

A Deep Q -Network-Based Collaborative Control Research for Smart Ammunition Formation

Leader–follower UAVs formation control based on a deep Q-network collaborative framework

Design of intelligent transportation verification platform based on hardware in the loop simulation technology

Contact Info

Product

Resources

About

A Deep $Q$ -Network-Based Collaborative Control Research for Smart Ammunition Formation

A Deep $Q$ -Network-Based Collaborative Control Research for Smart Ammunition Formation