首页|Extremum seeking control for UAV close formation flight via improved pigeon-inspired optimization

Extremum seeking control for UAV close formation flight via improved pigeon-inspired optimization

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This paper proposes a comprehensive design scheme for the extremum seeking control(ESC)of the unmanned aerial vehicle(UAV)close formation flight.The proposed design scheme combines a Newton-Raphson method with an extended Kalman filter(EKF)to dynamically estimate the optimal position of the following UAV relative to the leading UAV.To reflect the wake vortex effects reliably,the drag coefficient induced by the wake vortex is considered as a performance function.Then,the performance function is parameterized by the first-order and second-order terms of its Taylor series expansion.Given the excellent performance of nonlinear estimation,the EKF is used to estimate the gradient and the Hessian matrix of the parameterized performance function.The output feedback of the proposed scheme is determined by iterative calculation of the Newton-Raphson method.Compared with the traditional ESC and the classic ESC,the proposed design scheme avoids the slow continuous time integration of the gradient.This allows a faster convergence of relative position extremum.Furthermore,the proposed method can provide a smoother command during the seeking process as the second-order term of the performance function is taken into account.The convergence analysis of the proposed design scheme is accomplished by showing that the output feedback is a supermartingale sequence.To improve estimation performance of the EKF,a improved pigeon-inspired optimization(IPIO)is proposed to automatically tune the noise covariance matrix.Monte Carlo simulations for a three-UAV close formation show that the proposed design scheme is robust to the initial position of the following UAV.

unmanned aerial vehicleclose formationextremum seeking controlNewton-Raphson methodimproved pigeon-inspired optimization

YUAN GuangSong、DUAN HaiBin

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State Key Laboratory of Virtual Reality Technology and Systems,School of Automation Science and Electrical Engineering,Beihang University(BUAA),Beijing 100083,China

National Natural Science Foundation of ChinaNational Natural Science Foundation of ChinaNational Natural Science Foundation of ChinaNational Natural Science Foundation of ChinaOpen Fund/Postdoctoral Fund of the Laboratory of Cognition and Decision Intelligence for Complex Systems,Institute of Automation

91948204U20B2071T2121003U1913602CASIA-KFKT-08

2024

10.1007/s11431-023-2463-0

中国科学:技术科学(英文版)
中国科学院

中国科学:技术科学(英文版)

CSTPCDEI
影响因子：1.056
ISSN：1674-7321
年,卷(期)：2024.67(2)
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