Mechatronics, Automation, and Control Systems Laboratory

(MACS Lab) (Move, Act, Create, Serve)

The MACS Lab investigates theories and practice of dynamic systems and controls, to seek better understanding and engineering of the systematic interplay between data, system, and control in machines and automation processes that positively impact our lives.
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Control-oriented system modeling and identification, information fusion, adaptive and learning controls, robust control, precision mechatronics, and optimization.

Sample projects

Additive manufacturing, agile robots, semiconductor manufacturing, nm-scale precision control, vision-based control, human-machine interaction, and vibration rejection.


National Science Foundation, Department of Energy, NASA, Department of Defense, UTC Institute of Advanced Systems Engineering, and industries.

Selected Research Outcomes

Work from the MACS lab has been supported by the National Science Foundation, UTC Institute of Advanced Systems Engineering, Department of Energy, Department of Defense, NASA, and industries. Dr. Chen is a recipient of the National Science Foundation CAREER award, the SME Sandra L. Bouckley Outstanding Young Manufacturing Engineer Award, and the Young Investigator Award from the ISCIE / ASME International Symposium on Flexible Automation. Members of the MACS lab have received Best Paper Award from the International Symposium on Flexible Automation, Best Vibrations Paper Award, Best Student Paper Awards on Mechatronics and Robotics from the ASME Dynamic Systems and Control Division, Best Senior Design, and best paper in session awards in various conferences.

research snapshots

We conduct systematic research on mass customization, short-run and high-value manufacturing, and controls of complex systems.

Additive manufacturing

Control in different time scales: from precision laser-material interaction for aerospace and medical application to process reconfiguration and reclaiming materials.

robotics and learning FROM limited feedback

Robot control and collaborative sensing for systematic fast control under slow e.g. vision feedback.

Adaptive control

We were among the 3 teams that achieved the top results in an international benchmark on adaptive regulation.

Recent news

MECC concluded with a splashing success! As the first in-person event of the conference series, MECC 2022 attracted more than 260 registrations and disseminated 195 presentations! Thank you everyone for the great support!

The award is for pioneering methods and analyses of digital control parameterizations for precision mechatronic systems, advanced manufacturing, and their education and industrial adoption.

The new grant will advance artificial intelligence and robotics research.


Xu Chen

Bryan T. McMinn Endowed Associate Professor

Department of Mechanical Engineering

University of Washington