Autonomous Reconfiguration Control Method for UAV's Formation Based on Nash Bargain

Ru, Chang-Jian; Wei, Ruixuan; Dai, Jing Min; Dong, Shuai; Zhang, Lipeng

doi:10.3724/sp.j.1004.2013.01349

Cited by 7 publications

(7 citation statements)

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“…In formula (3)(4)(5)(6)(7)(8)(9)(10)(11)(12), F min (X ) is the minimum cost of the population G = [g 1 , g 2 , . .…”

Section: Convergence Proof Of Pre-dmpc-de Algorithm For Dmpcmentioning

confidence: 99%

“…The more drones in the formation, the explosive growth the potential energy function will be. Ru [12] et al designed a Distributed Model Predictive Control (DMPC) method based on Nash bargaining to solve the reconfiguration function of the multi-UAV formation. However, it does not take the communication distance constraint of UAVs into the formation mathematical model.…”

Section: Introductionmentioning

confidence: 99%

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Trajectory Following and Improved Differential Evolution Solution for Rapid Forming of UAV Formation

Bian

Sun

2019

IEEE Access

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In this paper we proposed the circle trajectory assembly algorithm to control the multi-UAVs circular assembly formation. CTFAP solution provides rapid formation of UAVs on a circular orbit and solve the problem of large scattering distance. Proposed Distributed model prediction control framework improves the optimization ability and reduces the computation consumption with the better convergence ability of the UAV formation. Firstly, a circular trajectory following algorithm with an adaptive parameter is proposed to complete the rapid formation of UAVs on a circular orbit and solve the problem of large scattering distance during formation forming. Then, in the stage of formation reconfiguration, with distributed model prediction control framework (DMPC), the proposed method gets the prediction information of DMPC to optimize the population of classical differential evolution (DE) algorithm and improve the iterative optimization ability of DE algorithm. Experiments show that the proposed differential evolution algorithm greatly improves the efficiency of solving the formation reconfiguration problem under the DMPC framework and overcomes the disadvantages of random population of classical DE. For the proposed rapid forming method, assembly range is reduced by 41% compared with direct linearly formation assembly, and the formation forming time is reduced by approximately 21%. Compared with other optimization algorithms such as Particle Swarm Optimization (PSO), Genetic Algorithm (GA) and DE, the proposed differential evolution algorithm reduces instruction response time of single-drones by 16%-30%.

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“…In formula (3)(4)(5)(6)(7)(8)(9)(10)(11)(12), F min (X ) is the minimum cost of the population G = [g 1 , g 2 , . .…”

Section: Convergence Proof Of Pre-dmpc-de Algorithm For Dmpcmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Trajectory Following and Improved Differential Evolution Solution for Rapid Forming of UAV Formation

Bian

Sun

2019

IEEE Access

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“…In the process of penetration multiple UAVs formation will face a variety of threats, this paper takes the early warning radar, short-range air defense radar, and no fly zone (including fixed obstacles, fixed threats, and mobile threats), assuming that all the UAVs in formation are able to detect enemy early warning radar and air defense radar, and they carry jamming equipment which is used to interfere in the early warning radar and air defense radar. Figure 2, assuming that the early warning radar's position is ( je , je ), the early warning radar jamming equipment position is ( je , je ), the effective interference distance of early warning radar jamming equipment is ewr , and the max detection radium of early warning radar is max , when a certain UAV is interfering the early warning radar, its detection radium can be described as [39,40]…”

Section: Description Of Formation Reconfigurationmentioning

confidence: 99%

Multi-UAVs Formation Autonomous Control Method Based on RQPSO-FSM-DMPC

Zhou¹,

Kang²,

Dai³

et al. 2016

Mathematical Problems in Engineering

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For various threats in the enemy defense area, in order to achieve covert penetration and implement effective combat against enemy, the unmanned aerial vehicles formation needs to be reconfigured in the process of penetration; the mutual collision avoidance problems and communication constraint problems among the formation also need to be considered. By establishing the virtual-leader formation model, this paper puts forward distributed model predictive control and finite state machine formation manager. Combined with distributed cooperative strategy establishing the formation reconfiguration cost function, this paper proposes that adopting the revised quantum-behaved particle swarm algorithm solves the cost function, and it is compared with the result which is solved by particle swarm algorithm. Simulation result shows that this algorithm can control multiple UAVs formation autonomous reconfiguration effectively and achieve covert penetration safely.

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“…When a single UAV accomplishes tasks individually, it will be more likely to reduce mission success, due to its limited information accessing ability. In comparison, multiple UAVs collaborating with each other maintain a certain formation during the flight, which provides them with full access to environmental information, increases resistance to external attack capability, improves working efficiency and robustness of the system, and so forth, so it has attracted wide attention [1][2][3]. Formation flight is an important aspect of multiple UAVs cooperative control.…”

Section: Introductionmentioning

confidence: 99%

Multimodel Predictive Control Approach for UAV Formation Flight

Wei

Wang

et al. 2014

Mathematical Problems in Engineering

Self Cite

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Formation flight problem is the most important and interesting problem of multiple UAVs (unmanned aerial vehicles) cooperative control. In this paper, a novel approach for UAV formation flight based on multimodel predictive control is designed. Firstly, the state equation of relative motion is obtained and then discretized. By the geometrical method, the characteristic points of state are determined. Afterwards, based on the linearization technique, the standard linear discrete model is obtained at each characteristic state point. Then, weighted model set is proposed using the idea of T-S (Takagi-Sugeno) fuzzy control and the predictive control is carried out based on the multimodel method. Finally, to verify the performance of the proposed method, two different simulation scenarios are performed.

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Autonomous Reconfiguration Control Method for UAV's Formation Based on Nash Bargain

Cited by 7 publications

References 0 publications

Trajectory Following and Improved Differential Evolution Solution for Rapid Forming of UAV Formation

Trajectory Following and Improved Differential Evolution Solution for Rapid Forming of UAV Formation

Multi-UAVs Formation Autonomous Control Method Based on RQPSO-FSM-DMPC

Multimodel Predictive Control Approach for UAV Formation Flight

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