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Dong Z.,Beihang University | Dong Z.,Science and Technology on Electronic optic Control Laboratory | Chen Z.,Beihang University | Zhou R.,Beihang University | Zhang R.,Flight automatic control research institute
Proceedings of the 2011 6th IEEE Conference on Industrial Electronics and Applications, ICIEA 2011 | Year: 2011

This article is concerned with the UAV path re-planning problem to be solved by using a novel approach based on the hybrid of virtual force and A * search algorithm (HVFA). The formulation of the UAV path re-planning is presented, and the hybrid system model of virtual force (VF) is given. The scheme of path re-planning is designed and the corresponding computational complexity is analyzed quantitatively. Simulation results prove the feasibility and usefulness of using HVFA for UAV path re-planning. Performance comparisons between the HVFA method and the fuzzy virtual force (FVF) method demonstrate that the proposed approach is fast enough to meet the real-time requirements of the online path planning problems. © 2011 IEEE.


Xue Y.,Nanjing University of Aeronautics and Astronautics | Zhuang Y.,Nanjing University of Aeronautics and Astronautics | Ni T.,No.723 Institute of China Shipbuilding Industry Corporation | Ouyang J.,Nanjing University of Aeronautics and Astronautics | Wang Z.,Science and Technology on Electronic optic Control Laboratory
Journal of Systems Engineering and Electronics | Year: 2012

There are many population-based stochastic search algorithms for solving optimization problems. However, the universality and robustness of these algorithms are still unsatisfactory. This paper proposes an enhanced self-adaptive evolutionary algorithm (ESEA) to overcome the demerits above. In the ESEA, four evolutionary operators are designed to enhance the evolutionary structure. Besides, the ESEA employs four effective search strategies under the framework of the self-adaptive learning. Four groups of the experiments are done to find out the most suitable parameter values for the ESEA. In order to verify the performance of the proposed algorithm, 26 state-of-the-art test functions are solved by the ESEA and its competitors. The experimental results demonstrate that the universality and robustness of the ESEA outperform its competitors.


Wang Z.,Science and Technology on Electronic optic Control Laboratory | Li J.,Nanjing University of Aeronautics and Astronautics | Liu M.,Nanjing University of Aeronautics and Astronautics | Xue Y.,Nanjing University of Aeronautics and Astronautics | Zhuang Y.,Nanjing University of Aeronautics and Astronautics
Journal of Convergence Information Technology | Year: 2012

A new multi-target tracking method that combines the fuzzy spectral clustering data association method and particle filter is presented. Firstly, the fuzzy spectral clustering approach is provided to deal with the data association problem that arises due to the uncertainty of the measurements, which can eliminate invalidate measurements. In order to deal with the state estimation problem in non- Gaussian dynamic system, particle filter and joint association innovations are employed to update each target state independently. Finally, the proposed algorithm is applied to the multi-target tracking problem. Simulation results demonstrate the effectiveness of the algorithm.


Duan H.B.,Beihang University | Yu Y.X.,Beihang University | Zhao Z.Y.,Science and Technology on Electronic optic Control Laboratory
Science China Information Sciences | Year: 2013

With the improvement of the aircraft flight performance and development of computing science, uninhabited combat aerial vehicle (UCAV) could accomplish more complex tasks. But this also put forward stricter requirements for the flight control system, which are the crucial issues of the whole UCAV system design. This paper proposes a novel UCAV flight controller parameters identification method, which is based on predator-prey particle swarm optimization (PSO) algorithm. A series of comparative experimental results verify the feasibility and effectiveness of our proposed approach in this paper, and a predator-prey PSO-based software platform for UCAV controller design is also developed. © 2013 Science China Press and Springer-Verlag Berlin Heidelberg.


Zhao Z.,Northwestern Polytechnical University | Lu G.,Science and Technology on Electronic optic Control Laboratory
International Journal of Intelligent Computing and Cybernetics | Year: 2012

Purpose: The purpose of this paper is to present a hybrid method of intelligent optimization algorithm and Receding Horizon Control. The method is applied to solve the problem of cooperative search of multi-unmanned aerial vehicles (multi-UAVs). Design/methodology/approach: The intelligent optimization of Differential Evolution (DE) makes the complex problem of multi-UAVs cooperative search a regular function optimization problem. To meet the real-time requirement, the idea of Receding Horizon Control is applied. An Extended Search Map based on hormone information is used to describe the uncertain environment information. Findings: Simulation results indicate effectiveness of the hybrid method in solving the problem of cooperative search for multi-UAVs. Originality/value: The paper presents an interesting hybrid method of DE and Receding Horizon Control for the problem of cooperative multi-UAVs. © Emerald Group Publishing Limited.

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