Guangze Yang

Distributed Robust Time-Varying Formation Control for Multi-Agent Systems under Disturbances

Authors: Guang-Ze Yang¹ and Zi-Jiang Yang²

Abstract​

This work considers the problem of time-varying formation tracking control of second-order multi-agent systems under disturbances. The DR-TVFC (Distributed Robust Time-Varying Formation Control) approach is proposed, including distributed finite-time estimators of the leader's states and sliding mode time-varying formation controllers.

:::info Publication Status This work has been accepted by the 2024 63rd Annual Conference of SICE. :::

Problem and Background​

Titanic - Machine Learning from Disaster

Problem: Create a machine learning model to predict which passengers survived the Titanic disaster.

Background: According to the Kaggle competition "Titanic - Machine Learning from Disaster" description: "On April 15, 1912, during her maiden voyage, the widely considered 'unsinkable' RMS Titanic sank after colliding with an iceberg. Unfortunately, there weren't enough lifeboats for everyone onboard, resulting in the death of 1502 out of 2224 passengers and crew."

While there was an element of luck involved, certain groups of people were more likely to survive than others, such as women, children, and the upper-class.

Machine Learning Final Report Name: YANG GUANGZE Student ID: 20T1126N

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