An autonomous aerial combat framework for two-on-two engagements based on basic fighter maneuvers

Shin, Heemin; Lee, Jae-Hyun; Kim, Hyungi; Shim, David Hyunchul

doi:10.1016/j.ast.2017.11.014

Cited by 49 publications

(10 citation statements)

References 14 publications

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“…Additionally, directly applying these complex algorithms to a 6-DoF nonlinear model has been often discarded because of its demanding computational requirements. To address this limitation, Shin et al [29] proposed a basic flight manoeuvre (BFM)-based approach to guide the attacker and simulate a 6-DoF nonlinear UCAV model in an aerial combat environment. These approaches combine a 3D point mass model with a control law design based on nonlinear dynamic inversion (NDI) to control the three-dimensional flight path of a 6-DoF nonlinear UCAV model [22].…”

Section: Applications To Unmanned Combat Aerial Vehicles (Ucavs)mentioning

confidence: 99%

A deep reinforcement learning control approach for high-performance aircraft

De Marco,

D’Onza,

Manfredi

2023

Nonlinear Dyn

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This research introduces a flight controller for a high-performance aircraft, able to follow randomly generated sequences of waypoints, at varying altitudes, in various types of scenarios. The study assumes a publicly available six-degree-of-freedom (6-DoF) rigid aeroplane flight dynamics model of a military fighter jet. Consolidated results in artificial intelligence and deep reinforcement learning (DRL) research are used to demonstrate the capability to make certain manoeuvres AI-based fully automatic for a high-fidelity nonlinear model of a fixed-wing aircraft. This work investigates the use of a deep deterministic policy gradient (DDPG) controller agent, based on the successful applications of the same approach to other domains. In the particular application to flight control presented here, the effort has been focused on the design of a suitable reward function used to train the agent to achieve some given navigation tasks. The trained controller is successful on highly coupled manoeuvres, including rapid sequences of turns, at both low and high flight Mach numbers, in simulations reproducing a prey–chaser dogfight scenario. Robustness to sensor noise, atmospheric disturbances, different initial flight conditions and varying reference signal shapes is also demonstrated.

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Section: Applications To Unmanned Combat Aerial Vehicles (Ucavs)mentioning

confidence: 99%

A deep reinforcement learning control approach for high-performance aircraft

De Marco,

D’Onza,

Manfredi

2023

Nonlinear Dyn

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“…High-speed unmanned aerial vehicle (UAV) is a general term for a class of aircraft used as precision-guided weapons to attack enemy targets, such as the suicide UAV, missile, and so on [ 1 , 2 ]. Compared with the manned fighter, UAV has the advantages of fast dispatch speed, strong mobility, zero casualties, and convenient multi aircraft cooperative operation [ 3 – 5 ]. In particular, UAVs have been highly valued by military forces all over the world, with the successful application of combat UAVs, for example, “MQ-1 Predator” of the U.S. military has been applied in a variety of practical battlefields, such as Pakistan's antiterrorism, Afghanistan war, and Iraq war [ 6 , 7 ].…”

Section: Introductionmentioning

confidence: 99%

Intelligent Guidance and Control Methods for Missile Swarm

Huang

Zhao

Zhang

2022

Computational Intelligence and Neuroscience

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High-speed unmanned aerial vehicles (UAVs) are more and more widely used in both military and civil fields at present, especially the missile swarm attack, and will play an irreplaceable key role in the future war as a special combat mode. This study summarizes the guidance and control methods of missile swarm attack operation. First, the traditional design ideas of the guidance and control system are introduced; then, the typical swarm attack guidance and control methods are analyzed by taking their respective characteristics into considering, and the limitations of the traditional design methods are given. On this basis, the study focuses on the advantages of intelligent integrated guidance and control design over traditional design ideas, summarizes the commonly used integrated guidance and control design methods and their applications, and explores the cooperative attack strategy of missile swarm suitable for the integrated guidance and control system. Finally, the challenges of missile swarm guidance and control are described, and the problems worthy of further research in the future are prospected. Summarizing the guidance and control methods of missile will contribute to the innovative research in this field, so as to promote the rapid development of unmanned swarm attack technology.

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“…For instance, the cooperative hazardous source searching method was studied using multiple mobile agents in [1] while an attack strategy of multiple UAVs against an enemy ship was investigated in [2]. The recent achievement on the MAS also can be found in the formation control [3][4][5][6], flocking [7][8][9], cooperative tactics and planning [10][11][12][13][14], or standoff target tracking [15].…”

Section: Introductionmentioning

confidence: 99%

Adversarial Swarm Defence Using Multiple Fixed-Wing Unmanned Aerial Vehicles

Choi¹,

Seo

Shin

et al. 2022

IEEE Trans. Aerosp. Electron. Syst.

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This paper proposes a novel coverage-based adversarial swarm defence algorithm. The defender swarm composed of fixed-wing unmanned aerial vehicles (UAVs) is assumed to have explosives onboard to intercept an adversarial swarm. The proposed approach consists of two steps: i) impact point optimization and ii) model predictive control (MPC)-based impact time control. The impact point optimization periodically optimizes impact points for the corresponding UAVs to maximize the coverage within the hostile swarm while minimizing the common impact time. The optimization domain is limited to a physically reachable area of UAVs with the common impact time. Besides, the MPC-based impact time controller is designed to ensure the multiple UAVs to arrive the generated time-varying impact points simultaneously. Numerical simulations are performed to prove the feasibility and efficiency of the proposed swarm defence algorithm.

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An autonomous aerial combat framework for two-on-two engagements based on basic fighter maneuvers

Cited by 49 publications

References 14 publications

A deep reinforcement learning control approach for high-performance aircraft

A deep reinforcement learning control approach for high-performance aircraft

Intelligent Guidance and Control Methods for Missile Swarm

Adversarial Swarm Defence Using Multiple Fixed-Wing Unmanned Aerial Vehicles

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