Robust Coordinated Hybrid Source Seeking With Obstacle Avoidance in Multivehicle Autonomous Systems
Robust Coordinated Hybrid Source Seeking With Obstacle Avoidance in Multivehicle Autonomous Systems
复制标题
多车辆自主系统中具有避障功能的鲁棒协调混合寻源
DOI：
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发表时间：
2021
影响因子：
6.8
通讯作者：
R. Sanfelice
中科院分区：
文献类型：
--
作者：
J. Poveda;M. Benosman;A. Teel;R. Sanfelice
在未知或敌对环境下运行的多车自主系统中，同时实现寻源和避障是一项具有挑战性的任务。事实上，即使对于单车辆系统，基于导航或障碍函数的光滑时不变反馈控制器也被证明极易受到任意小的干扰信号的影响，这些干扰信号可能会导致闭环系统不稳定，或者能够稳定操作空间中的虚假平衡。当源的位置进一步未知时，基于平均理论的自适应平滑寻源动力学也可能受到类似的限制。在本文中，我们通过引入一类新型的分布式混合无模型控制器来解决这一问题，该控制器在多车自主系统中实现了鲁棒的源搜索和避障，其中车辆具有非线性连续时间动态特性，可通过混合反馈稳定。混合寻源律在一组无梯度控制器之间切换，这些控制器由满足温和指数假设的势场导出。利用李雅普诺夫工具和集值混合动力系统的奇异摄动理论分析了闭环系统的稳定性和鲁棒性。通过数值和实验验证了理论结果。
In multivehicle autonomous systems that operate under unknown or adversarial environments, it is a challenging task to simultaneously achieve source seeking and obstacle avoidance. Indeed, even for single-vehicle systems, smooth time-invariant feedback controllers based on navigation or barrier functions have been shown to be highly susceptible to arbitrarily small jamming signals that can induce instability in the closed-loop system, or that are able to stabilize spurious equilibria in the operational space. When the location of the source is further unknown, adaptive smooth source seeking dynamics based on averaging theory may suffer from similar limitations. In this article, we address this problem by introducing a class of novel distributed hybrid model-free controllers, that achieve robust source seeking and obstacle avoidance in multivehicle autonomous systems, with vehicles characterized by nonlinear continuous-time dynamics stabilizable by hybrid feedback. The hybrid source seeking law switches between a family of cooperative gradient-free controllers, derived from potential fields that satisfy mild invexity assumptions. The stability and robustness properties of the closed-loop system are analyzed using Lyapunov tools and singular perturbation theory for set-valued hybrid dynamical systems. The theoretical results are validated via numerical and experimental tests.
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10.1016/j.automatica.2017.05.002
发表时间：
2017-09
期刊：
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影响因子：
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作者：
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影响因子：
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作者：
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DOI：
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发表时间：
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期刊：
IEEE Conference on Decision and Control
影响因子：
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作者：
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DOI：
10.23919/acc.2018.8431064
发表时间：
2018
期刊：
American Control Conference
影响因子：
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作者：
Poveda, Jorge I.;Benosman, Mouhacine;Teel, Andrew R.;Sanfelice, Ricardo G.
通讯作者：
Sanfelice, Ricardo G.