基于梯度估计和群体控制的协同寻源(第一部分)

Esteban Rosero, H. Werner
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引用次数: 23

摘要

本文及其配套论文[14]研究了移动智能体组成的协同寻源问题。每个agent配备位置和信号强度测量传感器;他们的任务是求标量场的最大值。代理通过通信网络与相邻代理交换信息。在这两篇论文的第一部分,给出了每个智能体的分布式梯度估计和单和双积分器模型的分散导航控制器。当测量信号被噪声干扰时,采用分布式一致性滤波器估计梯度方向。该策略基于梯度估计算法和编队控制器。提供了稳定条件。数值仿真验证了所提控制律的有效性。第2部分将此方法扩展到一般线性定常模型。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Cooperative source seeking via gradient estimation and formation control (Part 1)
In this paper and its companion paper [14], the problem of cooperative source seeking by a formation of mobile agents is considered. Each agent is equipped with position and signal strength measurement sensors; their task is to find the maximum of the scalar field. Agents exchange information with neighboring agents through a communication network. In the first part of this couple of papers, a distributed gradient estimation for each agent and a decentralized navigation controller for single- and double-integrator models are presented. When the signal measurements are corrupted by noise, distributed consensus filters are used in order to estimate the gradient direction. The strategy is based on both a gradient estimating algorithm and a formation controller. Stability conditions are provided. Numerical simulations illustrate the effectiveness of the proposed control law. Part two extends this approach to general linear time-invariant models.
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