In this study, under the distributed control, the relevant information detected was used to realize UAV formation based on self-organizing behavior on the basis of the quasi-static communication topological structure....
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ISBN:
(纸本)9781450376259
In this study, under the distributed control, the relevant information detected was used to realize UAV formation based on self-organizing behavior on the basis of the quasi-static communication topological structure. This paper improves and optimizes the artificial potential energy function by combining the consistency algorithm and virtual navigation method to satisfy the three constraints of the congestion control strategy. By adding the edge delay effect, the local congestion will not occur, and the network connectivity in the global process will not occur arbitrary switching of the topology structure, so that the UAVs can switch from any initial state to the expected formation pattern and maintain the pattern.
In this study, under the distributed control, the relevant information detected was used to realize UAV formation based on self-organizing behavior on the basis of the quasi-static communication topological structure....
详细信息
ISBN:
(纸本)9781450376259
In this study, under the distributed control, the relevant information detected was used to realize UAV formation based on self-organizing behavior on the basis of the quasi-static communication topological structure. This paper improves and optimizes the artificial potential energy function by combining the consistency algorithm and virtual navigation method to satisfy the three constraints of the congestion control strategy. By adding the edge delay effect, the local congestion will not occur, and the network connectivity in the global process will not occur arbitrary switching of the topology structure, so that the UAVs can switch from any initial state to the expected formation pattern and maintain the pattern.
Mobile crowdsensing is a promising service paradigm utilizing the pervasive mobile devices to cooperatively perform large-scale sensing tasks. Data uploading which aims to deliver the sensed data to the control center...
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formation flying means two or more flight vehicles maintain an organizational flight mode. In autonomous formation, the guidance is normally divided into formation configuration and task assignment. This paper is focu...
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formation flying means two or more flight vehicles maintain an organizational flight mode. In autonomous formation, the guidance is normally divided into formation configuration and task assignment. This paper is focused on the formation configuration of quadrotors from their initial random positions to an expected configuration in response of the leader quadrotor. Based on the position and velocity of the leader, the formation controller will generate the guidance and control instructions from the relative dynamic system. The updated positions of the followers are compared with the expected positions and furtherly processed by PI controllers to form speed instructions for the followers, and feedforward controllers are included to shape commands to provide better tracking performance. Simulations with five quadrotors result show that the designed guidance and control algorithm can help quickly achieve and keep the desired formation configuration even to follow complex motions of the leader quadrotor.
This paper presents a convergence analysis on a distributed Alternating Direction Method of Multipliers (ADMM) algorithm which solves online convex optimization problems under linear constraints. The goal is to distri...
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ISBN:
(纸本)9781467360906
This paper presents a convergence analysis on a distributed Alternating Direction Method of Multipliers (ADMM) algorithm which solves online convex optimization problems under linear constraints. The goal is to distributively optimize a global objective function over a network of decision makers. The global objective function is composed of convex cost functions associated with each agent. The local cost functions can be broken down into two convex functions, one of which is revealed over time to the decision makers and one known a priori. We extend an online ADMM algorithm to a distributed setting based on dual-averaging. We then explore the rate of convergence of the performance of the sequence of decisions generated by the algorithm to the best fixed decision in hindsight. This performance metric is called regret. An upper bound on the regret of the proposed algorithm is presented as a function of the underlying network topology and linear constraints. The online distributed ADMM algorithm is then applied to a formation acquisition problem.
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