In this paper, we design consensus algorithms for multiple unmanned aerial vehicles （UAV）. We mainly focus on the control design in the face of measurement noise and propose a position consensus controller based on ...In this paper, we design consensus algorithms for multiple unmanned aerial vehicles （UAV）. We mainly focus on the control design in the face of measurement noise and propose a position consensus controller based on the sliding mode control by using the distributed UAV information. Within the framework of Lyapunov theory, it is shown that all signals in the closed-loop multi- UAV systems are stabilized by the proposed algorithm, while consensus errors are uniformly ultimately bounded. Moreover, for each local UAV, we propose a mechanism to define the trustworthiness, based on which the edge weights are tuned to eliminate negative influence from stubborn agents or agents exposed to extremely noisy measurement. Finally, we develop software for a nano UAV platform, based on which we implement our algorithms to address measurement noises in UAV flight tests. The experimental results validate the effectiveness of the proposed algorithms.展开更多
基金This work was supported in part by the National Natural Science Foundation of China （No. 61633007, 61703112）, in part by the China Postdoctoral Science Foundation （No. 2016M600643） and the special fund （No. 2017T100618）, and in part by the Office of Naval Research （No. N00014-17-1-2239, NO0014-18-1-2221 ）.
文摘In this paper, we design consensus algorithms for multiple unmanned aerial vehicles （UAV）. We mainly focus on the control design in the face of measurement noise and propose a position consensus controller based on the sliding mode control by using the distributed UAV information. Within the framework of Lyapunov theory, it is shown that all signals in the closed-loop multi- UAV systems are stabilized by the proposed algorithm, while consensus errors are uniformly ultimately bounded. Moreover, for each local UAV, we propose a mechanism to define the trustworthiness, based on which the edge weights are tuned to eliminate negative influence from stubborn agents or agents exposed to extremely noisy measurement. Finally, we develop software for a nano UAV platform, based on which we implement our algorithms to address measurement noises in UAV flight tests. The experimental results validate the effectiveness of the proposed algorithms.
