add cdc news and vtol paper

Author: tkkim-robot

content/news/2025-cdc.md

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+---
+layout: news
+title:  Two papers accepted at <strong>CDC</strong> 2025
+date:  2025-07-15
+---
+
+Two papers have been accepted at IEEE CDC 2025! Check out: ["How to Adapt Control Barrier Functions? A Learning-Based Approach with Applications to a VTOL Quadplane"](/papers/2025-vtol-cdc.md) and ["Distributed Resilience-Aware Control in Multi-Robot Networks"](/papers/2025-resilience-aware_cdc.md).

content/papers/2025/2025-vtol-cdc.md

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+---
+layout: papers
+title:  "How to Adapt Control Barrier Functions? A Learning-Based Approach with Applications to a VTOL Quadplane"
+date: 2025-12-19
+image: /images/2025-vtol-cdc.gif
+venue: "IEEE CDC 2025"
+authors:
+    - taekyungkim
+    - Randal W. Beard
+    - dimitrapanagou
+projectpage: https://www.taekyung.me/how-to-adapt-cbf
+arxiv: https://arxiv.org/abs/2504.03038
+code: https://github.com/tkkim-robot/online_adaptive_cbf
+abstract: "In this paper, we present a novel theoretical framework for online adaptation of Control Barrier Function (CBF) parameters, i.e., of the class K functions included in the CBF condition, under input constraints. We introduce the concept of locally validated CBF parameters, which are adapted online to guarantee finite-horizon safety, based on conditions derived from Nagumo's theorem and tangent cone analysis. To identify these parameters online, we integrate a learning-based approach with an uncertainty-aware verification process that account for both epistemic and aleatoric uncertainties inherent in neural network predictions. Our method is demonstrated on a VTOL quadplane model during challenging transition and landing maneuvers, showcasing enhanced performance while maintaining safety."
+---