Under the structural LDS condition, a parameterized family of LTNs converges to a globally exponentially stable PDS in the fast limit and a globally asymptotically stable HSS in the slow limit.
Khalil,Nonlinear Systems, ser
2 Pith papers cite this work. Polarity classification is still indexing.
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Multi-robot teams achieve improved coverage in unknown environments by dynamically updating parametric models to guide ergodic trajectories based on real-time feedback.
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Timescale Limits of Linear-Threshold Networks
Under the structural LDS condition, a parameterized family of LTNs converges to a globally exponentially stable PDS in the fast limit and a globally asymptotically stable HSS in the slow limit.
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Mind the Gaps: Multi-Robot Feedback-Driven Ergodic Coverage in Unknown Environments
Multi-robot teams achieve improved coverage in unknown environments by dynamically updating parametric models to guide ergodic trajectories based on real-time feedback.