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Hardware-in-loop adaptive neural control for a tiltable V-tail morphing aircraft
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作者 Fu-xiang Qiao Jing-ping Shi +1 位作者 Xiao-bo Qu yong-xi lyu 《Defence Technology(防务技术)》 SCIE EI CAS CSCD 2023年第4期197-211,共15页
This paper proposes an adaptive neural control(ANC)method for the coupled nonlinear model of a novel type of embedded surface morphing aircraft which has a tiltable V-tail.A nonlinear model with sixdegrees-of-freedom ... This paper proposes an adaptive neural control(ANC)method for the coupled nonlinear model of a novel type of embedded surface morphing aircraft which has a tiltable V-tail.A nonlinear model with sixdegrees-of-freedom is established.The first-order sliding mode differentiator(FSMD)is applied to the control scheme to avoid the problem of“differential explosion”.Radial basis function neural networks are introduced to estimate the uncertainty and external disturbance of the model,and an ANC controller is proposed based on this design idea.The stability of the proposed ANC controller is proved using Lyapunov theory,and the tracking error of the closed-loop system is semi-globally uniformly bounded.The effectiveness and robustness of the proposed method are verified by numerical simulations and hardware-in-the-loop(HIL)simulations. 展开更多
关键词 Morphing aircraft Back-stepping control Adaptive control Neural networks Radial basis function
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A Multi-UCAV cooperative occupation method based on weapon engagement zones for beyond-visual-range air combat 被引量:6
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作者 Wei-hua Li Jing-ping Shi +2 位作者 Yun-yan Wu Yue-ping Wang yong-xi lyu 《Defence Technology(防务技术)》 SCIE EI CAS CSCD 2022年第6期1006-1022,共17页
Recent advances in on-board radar and missile capabilities,combined with individual payload limitations,have led to increased interest in the use of unmanned combat aerial vehicles(UCAVs)for cooperative occupation dur... Recent advances in on-board radar and missile capabilities,combined with individual payload limitations,have led to increased interest in the use of unmanned combat aerial vehicles(UCAVs)for cooperative occupation during beyond-visual-range(BVR)air combat.However,prior research on occupational decision-making in BVR air combat has mostly been limited to one-on-one scenarios.As such,this study presents a practical cooperative occupation decision-making methodology for use with multiple UCAVs.The weapon engagement zone(WEZ)and combat geometry were first used to develop an advantage function for situational assessment of one-on-one engagement.An encircling advantage function was then designed to represent the cooperation of UCAVs,thereby establishing a cooperative occupation model.The corresponding objective function was derived from the one-on-one engagement advantage function and the encircling advantage function.The resulting model exhibited similarities to a mixed-integer nonlinear programming(MINLP)problem.As such,an improved discrete particle swarm optimization(DPSO)algorithm was used to identify a solution.The occupation process was then converted into a formation switching task as part of the cooperative occupation model.A series of simulations were conducted to verify occupational solutions in varying situations,including two-on-two engagement.Simulated results showed these solutions varied with initial conditions and weighting coefficients.This occupation process,based on formation switching,effectively demonstrates the viability of the proposed technique.These cooperative occupation results could provide a theoretical framework for subsequent research in cooperative BVR air combat. 展开更多
关键词 Unmanned combat aerial vehicle Cooperative occupation Beyond-visual-range air combat Weapon engagement zone Discrete particle swarm optimization Formation switching
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Autonomous maneuver decision-making for a UCAV in short-range aerial combat based on an MS-DDQN algorithm 被引量:4
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作者 Yong-feng Li Jing-ping Shi +2 位作者 Wei Jiang Wei-guo Zhang yong-xi lyu 《Defence Technology(防务技术)》 SCIE EI CAS CSCD 2022年第9期1697-1714,共18页
To solve the problem of realizing autonomous aerial combat decision-making for unmanned combat aerial vehicles(UCAVs) rapidly and accurately in an uncertain environment, this paper proposes a decision-making method ba... To solve the problem of realizing autonomous aerial combat decision-making for unmanned combat aerial vehicles(UCAVs) rapidly and accurately in an uncertain environment, this paper proposes a decision-making method based on an improved deep reinforcement learning(DRL) algorithm: the multistep double deep Q-network(MS-DDQN) algorithm. First, a six-degree-of-freedom UCAV model based on an aircraft control system is established on a simulation platform, and the situation assessment functions of the UCAV and its target are established by considering their angles, altitudes, environments, missile attack performances, and UCAV performance. By controlling the flight path angle, roll angle, and flight velocity, 27 common basic actions are designed. On this basis, aiming to overcome the defects of traditional DRL in terms of training speed and convergence speed, the improved MS-DDQN method is introduced to incorporate the final return value into the previous steps. Finally, the pre-training learning model is used as the starting point for the second learning model to simulate the UCAV aerial combat decision-making process based on the basic training method, which helps to shorten the training time and improve the learning efficiency. The improved DRL algorithm significantly accelerates the training speed and estimates the target value more accurately during training, and it can be applied to aerial combat decision-making. 展开更多
关键词 Unmanned combat aerial vehicle Aerial combat decision Multi-step double deep Q-network Six-degree-of-freedom Aerial combat maneuver library
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