Reinforcement learning policies for quadrotor inversion transitions with bidirectional thrust outperform optimization baselines by 32% in position RMSE and 57% in settling time in simulation, with successful hardware validation.
Transportation Research Part C: Emerging Technologies 108, 130–150
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LMI-parametrized dynamic output-feedback controller synthesis from noisy I/O data for dissipativity and H2 performance in unknown discrete-time LTI systems, claimed non-conservative within the setting.
TA-ANP is a task-aware attentive neural process that performs global traffic state inference by fusing multi-source data, jointly solving three sub-tasks, and providing calibrated uncertainty estimates with resilience to changing sensor configurations.
A laminar cyber-physical design with standardized interfaces can translate device-level flexibility into reliable grid services across scales, as illustrated by U.S. and Danish pilots and operational deployments.
citing papers explorer
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AcroRL: Learning Aggressive Quadrotor Inversion using Bidirectional Thrust
Reinforcement learning policies for quadrotor inversion transitions with bidirectional thrust outperform optimization baselines by 32% in position RMSE and 57% in settling time in simulation, with successful hardware validation.
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Dynamic Output-Feedback Controller Synthesis for Dissipativity and $H_2$ Performance from Noisy Input-Output Data
LMI-parametrized dynamic output-feedback controller synthesis from noisy I/O data for dissipativity and H2 performance in unknown discrete-time LTI systems, claimed non-conservative within the setting.
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Metropolis-Scale Resilient and Trustworthy Traffic Flow Inference Using Multi-Source Data
TA-ANP is a task-aware attentive neural process that performs global traffic state inference by fusing multi-source data, jointly solving three sub-tasks, and providing calibrated uncertainty estimates with resilience to changing sensor configurations.
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Cross-Atlantic Research Agenda for Scalable Grid Architectures and Distributed Flexibility
A laminar cyber-physical design with standardized interfaces can translate device-level flexibility into reliable grid services across scales, as illustrated by U.S. and Danish pilots and operational deployments.