Automatic formation flight control

Dargan, J.; Pachter, Meir; D'Azzo, John

doi:10.2514/6.1992-4473

Cited by 26 publications

(10 citation statements)

References 2 publications

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“…Apply DE's mutation and recombination operators to x j , and generate the trial vector u j according to eqs. (6) and (8), and then compute its fitness function value f (u j ).…”

Section: Controller Designmentioning

confidence: 99%

“…The design and implementation of control methods for multiUAVs close formation is a hot issue recently. Based on the "Leader/Wingman" pattern, Proud, Dargan, and Buzogany [4,[6][7][8] addressed the problem of automatic formation flight control. They analyzed the additional aerodynamic coupling effects introduced by close formation, established the six-dimensional close formation equations and designed a proportional-integral (PI) formation-hold autopilot.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Receding horizon control for multi-UAVs close formation control based on differential evolution

Zhang¹,

Duan²,

Yuan³

2010

Sci. China Inf. Sci.

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Close formation flight is one of the most complicated problems on multi-uninhabited aerial vehicles (UAVs) coordinated control. Based on the nonlinear model of multi-UAVs close formation, a novel type of control strategy of using hybrid receding horizon control (RHC) and differential evolution algorithm is proposed. The issue of multi-UAVs close formation is transformed into several on-line optimization problems at a series of receding horizons, while the differential evolution algorithm is adopted to optimize control sequences at each receding horizon. Then, based on the Markov chain model, the convergence of differential evolution is proved. The working process of RHC controller is presented in detail, and the stability of close formation controller is also analyzed. Finally, three simulation experiments are performed, and the simulation results show the feasibility and validity of our proposed control algorithm.

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“…Apply DE's mutation and recombination operators to x j , and generate the trial vector u j according to eqs. (6) and (8), and then compute its fitness function value f (u j ).…”

Section: Controller Designmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Receding horizon control for multi-UAVs close formation control based on differential evolution

Zhang¹,

Duan²,

Yuan³

2010

Sci. China Inf. Sci.

View full text Add to dashboard Cite

show abstract

“…Compensation-type controllers [16][17][18][19][20][21], optimal 2 International Journal of Aerospace Engineering control [22][23][24][25], adaptive control [26,27], robust control [28], feedback linearization [27,[29][30][31], and behavioral [32] approaches have all been developed for formation flight applications for fixed wing aircraft and quadrotors.…”

Section: Introductionmentioning

confidence: 99%

Autonomous Close Formation Flight Control with Fixed Wing and Quadrotor Test Beds

Rice

Huo

et al. 2016

International Journal of Aerospace Engineering

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Autonomous formation flight is a key approach for reducing energy cost and managing traffic in future high density airspace. The use of Unmanned Aerial Vehicles (UAVs) has allowed low-budget and low-risk validation of autonomous formation flight concepts. This paper discusses the implementation and flight testing of nonlinear dynamic inversion (NLDI) controllers for close formation flight (CFF) using two distinct UAV platforms: a set of fixed wing aircraft named "Phastball" and a set of quadrotors named "NEO." Experimental results show that autonomous CFF with approximately 5-wingspan separation is achievable with a pair of low-cost unmanned Phastball research aircraft. Simulations of the quadrotor flight also validate the design of the NLDI controller for the NEO quadrotors.

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“…One of the problems of particular interest to researchers is the automatic control of a group of UCAVs flying in close formation [2,3] . Most of the research conducted in recent years has been focused on the coordination and station keeping of multiple UCAVs so as to maintain the relative separations and orientations between the UCAVs in the formation [4] and to track desired flight trajectories [5] .…”

Section: Introductionmentioning

confidence: 99%

Optimal Formation Reconfiguration Control of Multiple UCAVs Using Improved Particle Swarm Optimization

Duan

Luo

2008

J Bionic Eng

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Optimal formation reconfiguration control of multiple Uninhabited Combat Air Vehicles (UCAVs) is a complicated global optimum problem. Particle Swarm Optimization (PSO) is a population based stochastic optimization technique inspired by social behaviour of bird flocking or fish schooling. PSO can achieve better results in a faster, cheaper way compared with other bio-inspired computational methods, and there are few parameters to adjust in PSO. In this paper, we propose an improved PSO model for solving the optimal formation reconfiguration control problem for multiple UCAVs. Firstly, the Control Parameterization and Time Discretization (CPTD) method is designed in detail. Then, the mutation strategy and a special mutation-escape operator are adopted in the improved PSO model to make particles explore the search space more efficiently. The proposed strategy can produce a large speed value dynamically according to the variation of the speed, which makes the algorithm explore the local and global minima thoroughly at the same time. Series experimental results demonstrate the feasibility and effectiveness of the proposed method in solving the optimal formation reconfiguration control problem for multiple UCAVs.

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Automatic formation flight control

Cited by 26 publications

References 2 publications

Receding horizon control for multi-UAVs close formation control based on differential evolution

Receding horizon control for multi-UAVs close formation control based on differential evolution

Autonomous Close Formation Flight Control with Fixed Wing and Quadrotor Test Beds

Optimal Formation Reconfiguration Control of Multiple UCAVs Using Improved Particle Swarm Optimization

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