Guang-Ze Yang

Open Source · Educational Resource

A growing reference that puts mathematical explanations, code, and learning resources alongside each other.

Engineering · Educational Implementation

A collection of MATLAB implementations that connect model predictive control theory to quadratic-program formulations and constrained trajectory tracking.

Research · Publication

Safety-critical formation tracking asks how a multi-robot controller should respond when performance objectives and safety constraints become difficult to satisfy together.

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.

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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