Model Predictive Control Approaches for Dynamic Table Tennis Swinging

This thesis presents three model predictive control (MPC) formulations for robotic table tennis swinging, addressing the challenge of generating precise, real-time paddle trajectories for dynamic ball interactions. We explore key differences in optimization structure, solver strategy, and real-time implementation, evaluating each approach through hardware experiments that measure strike condition tracking and hit success. The final controller integrates the full task of a table tennis possession by planning the return ball trajectory through the contact dynamics, and generating a swing to achieve it. This controller improves the hit rate of the system from 88.3% to 97.6% and significantly enhances strike condition accuracy and smoothness enabling control over the landing location and spin of the ball.

Name thesis:degree_name

Master

Department dc:contributor.department

Massachusetts Institute of Technology. Department of Mechanical Engineering

Grantor dc:publisher

Massachusetts Institute of Technology

Year dc:date.issued

2025

Author dc:creator

• Nguyen, David H.

Advisor dc:contributor.advisor

• Kim, Sangbae

Statement dc:rights

• In Copyright - Educational Use Permitted
• Copyright retained by author(s)

Licence dc:rights.uri

• https://rightsstatements.org/page/InC-EDU/1.0/

Handle dc:identifier.uri

https://hdl.handle.net/1721.1/163436

OAI identifier oai:identifier

oai:dspace.mit.edu:1721.1/163436