7 citations · 7 across the 8 of their papers we have counts for
6 papers · 1 filter
Online Adaptation for Flying Quadrotors in Tight Formations
Pei-An Hsieh, Kong Yao Chee, M. Ani Hsieh
The task of flying in tight formations is challenging for teams of quadrotors because the complex aerodynamic wake interactions can destabilize individual team members as well as t…
Flying Quadrotors in Tight Formations using Learning-based Model Predictive Control
Kong Yao Chee, Pei-An Hsieh, George J. Pappas +1
Flying quadrotors in tight formations is a challenging problem. It is known that in the near-field airflow of a quadrotor, the aerodynamic effects induced by the propellers are com…
Enhancing Sample Efficiency and Uncertainty Compensation in Learning-based Model Predictive Control for Aerial Robots
Kong Yao Chee, Thales C. Silva, M. Ani Hsieh +1
The recent increase in data availability and reliability has led to a surge in the development of learning-based model predictive control (MPC) frameworks for robot systems. Despit…
LEARNEST: LEARNing Enhanced Model-based State ESTimation for Robots using Knowledge-based Neural Ordinary Differential Equations
Kong Yao Chee, M. Ani Hsieh
State estimation is an important aspect in many robotics applications. In this work, we consider the task of obtaining accurate state estimates for robotic systems by enhancing the…
Online Dynamics Learning for Predictive Control with an Application to Aerial Robots
Tom Z. Jiahao, Kong Yao Chee, M. Ani Hsieh
In this work, we consider the task of improving the accuracy of dynamic models for model predictive control (MPC) in an online setting. Although prediction models can be learned an…
KNODE-MPC: A Knowledge-based Data-driven Predictive Control Framework for Aerial Robots
Kong Yao Chee, Tom Z. Jiahao, M. Ani Hsieh
In this work, we consider the problem of deriving and incorporating accurate dynamic models for model predictive control (MPC) with an application to quadrotor control. MPC relies…