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Xu Chen

Faculty Photo

Director of the Boeing Advanced Research Collaboration

Bryan T. McMinn Endowed Research Professorship
Mechanical Engineering

Associate Professor
Mechanical Engineering

Pronouns: He/him


Dr. Xu Chen joined UW as an assistant professor of mechanical engineering in 2019. He received his M.S. and Ph.D. degrees in mechanical engineering from the University of California, Berkeley, and his Bachelor’s degree with honors from Tsinghua University, China.He pursues a research passion in dynamic systems and controls, to better understand and engineer smart machines and autonomy that positively impacts our lives. He builds control algorithms that counteract process variations and yield high-quality, agile manufacturing of complex parts at low unit costs compared to conventional machining. He also researches sensing, actuation, and energy transformation that facilitate novel machines and manufacturing processes: e.g., advisor robots for automated inspection in the aerospace industry. His work in laser-aided additive manufacturing advances aerospace components and custom-designed medical implants, with potential to improve more products for the energy, automotive, healthcare, and biomedical industries. He brought his technology to precision control and information storage industries, including developing multiple new servo designs for Western Digital Corporation’s industrial mass production.

Dr. Chen’s work – funded by NSF, DOE, DOD, state, and industries – has led to Best Paper Awards, first-tier adaptive control methods in international benchmark evaluations, and the graduation of two University Scholars. Dr. Chen is a recipient of the NSF CAREER Award, the SME Sandra L. Bouckley Outstanding Young Manufacturing Engineer Award, the Young Investigator Award from ISCIE / ASME International Symposium on Flexible Automation, the inaugural UTC Institute for Advanced Systems Engineering Breakthrough Award in 2016, and the 2017 UConn University Teaching Fellow Award Nominee. Dr. Chen is Program Chair of the 2022 Modeling, Estimation, and Control Conference sponsored by the American Automatic Control Council (AACC) and co-sponsored by the International Federation of Automatic Control (IFAC); Program Chair of the 2024 ISCIE/ASME International Symposium on Flexible Automation (ISFA); Publicity and Local Arrangements Chair of the 2020 and the 2023 IEEE/ASME International Conferences on Advanced Intelligent Mechatronics, and Exhibits Chair of the 2021 IEEE American Control Conference.


  • Ph.D, in Mechanical Engineering, University of California, Berkeley, 2013
  • M.S. in Mechanical Engineering, University of California, Berkeley, 2010
  • B.S. in Mechanical Engineering, Tsinghua University, 2008

Previous appointments

  • Assistant Professor, University of Connecticut

Current projects

Robotic Inspection of Complex Metalic Parts

In the $72-billion (in 2020 dollar) aerospace engine industry, overlooking defects as minor as scratches and pits could lead to imbalances in airflow and part fatigue, and as a result, premature engine wear and even engine failures. Current inspections limit the manufacturing process flow: not only is inspecting complex shiny surfaces tiring and time-consuming, but the inspection process is also burdensome, subjective, and requires years of training, particularly for high-volume production with outputs of 500+ parts per day per site. Integrating lighting physics, controlled environment data collection, and machine learning, Prof. Chen is leading a $1.3M collaborative project to build a high-quality, consistent surface profiling and fault identification to continuously improve inspection performance using accumulating data. Initial results have already achieved a first-of-its-kind controlled-environment data collection and machine classification of the challenging defects beyond the human limit.

CAREER: Adding to the Future: Thermal Modeling, Sparse Sensing, and Integrated Controls for Precise and Reliable Powder Bed Fusion

In contrast to conventional machining, where parts are made by cutting away unwanted material, additive manufacturing -- also called 3D printing -- builds three-dimensional objects of unprecedented complexity by progressively adding small amounts of material. Powder bed fusion (PBF), in which new material is added to the part being fabricated by applying and selectively melting a powdered feedstock, is a popular form of AM for fabricating complex metallic or high-performance polymeric parts. This project supports fundamental research to create new thermal modeling, sensing, and control algorithms that will lead to precise and reliable PBF. 

Fast Situational Awareness and Reliable Response with Heterogeneous Feedback and Number-Theoretic Control Primitives

This NSF-funded research aims to enable engineered systems to effectively rely on and respond to real-time data collected from multiple diverse and asynchronous sources, with applications to medical device technology and aerospace manufacturing platforms, thereby promoting the progress of science, and advancing the national prosperity. Rapid and uniform timekeeping is the current dogma for building real-time systems. Such systems, however, increasingly operate in the presence of ubiquitous and diverse forms of data and asynchronous, often irregular workflows. Foundational knowledge about how to leverage such data streams in designing and controlling the behavior of the next generation of critical systems, including those meant to assist humans in data-intensive situations, is lacking. This project bridges this knowledge gap by building a new theoretical and algorithmic framework that demonstrates how heterogeneous, often slow measurements can be reliably and quickly combined to achieve fast information retrieval and a robust feedback response. This framework will enable unsurpassed situational awareness and response agility beyond existing boundaries of process monitoring. A suite of related interactive demonstrations and dynamic visualizations will be developed and disseminated through web applications to foster a mindset of algorithmic thinking in students of all ages. Additional educational impact will result from integration of research results in advanced technical coursework and engagement of undergraduate and graduate students in research.

Select publications

  1. H. Xiao, Y. Bar-Shalom, and X. Chen. “A Collaborative Sensing and Model-based Realtime Recovery of Fast Temporal Flows from Sparse Measurements”. In: IEEE Transactions on Industrial Electronics 67.8 (Aug. 2020), pp. 6806–6814.
  2. D. Wang and X. Chen. “A Multirate Fractional-Order Repetitive Control for Laser-Aided Additive Manufacturing”. In: Control Engineering Practice 77 (2018), pp. 41–51. issn: 0967-0661.
  3. D. Wang and X. Chen. “A Spectral Analysis and Its Implications of Feedback Regulation beyond Nyquist Frequency”. In: IEEE/ASME Transactions on Mechatronics 23.2 (Apr. 2018), pp. 916–926.
  4. X. Chen and H. Xiao. “Multirate Forward-model Disturbance Observer for Feedback Regulation beyond Nyquist Frequency”. In: Systems & Control Letters 94 (Aug. 2016), pp. 181–188.
  5. X. Chen, T. Jiang, and M. Tomizuka. “Pseudo Youla-Kucera Parameterization with Control of the Waterbed Effect for Local Loop Shaping”. In: Automatica 62 (2015), pp. 177–183.
  6. X. Chen and M. Tomizuka. “Overview and New Results in Disturbance Observer based Adaptive Vibration Rejection with Application to Advanced Manufacturing”. In: International Journal of Adaptive Control and Signal Processing 29 (2015), pp. 1459–1474. issn: 1099-1115.
  7. X. Chen and M. Tomizuka. “New Repetitive Control with Improved Steady-State Performance and Accelerated Transient”. In: IEEE Transactions on Control Systems Technology 22.2 (Mar. 2014), pp. 664– 675. issn: 1063-6536.
  8. X. Chen and M. Tomizuka. “Optimal Decoupled Disturbance Observers for Dual-input Single-output Systems”. In: ASME Journal of Dynamic Systems, Measurement, and Control 136.5 (2014), p. 051018.
  9. X. Chen and M. Tomizuka. “Selective Model Inversion and Adaptive Disturbance Observer for Time- varying Vibration Rejection on an Active-suspension Benchmark”. In: European Journal of Control 19.4 (2013), pp. 300–312. issn: 0947-3580.
  10. X. Chen and M. Tomizuka. “A Minimum Parameter Adaptive Approach for Rejecting Multiple Narrow- band Disturbances with Application to Hard Disk Drives”. In: IEEE Transactions on Control Systems Technology 20.2 (Mar. 2012), pp. 408–415. issn: 1063-6536.
  11. F. Yang , T. Jiang , G. Lalier, J. Bartolone, and X. Chen. “A Process Control and Interlayer Heating Approach to Reuse Polyamide 12 Powders and Create Parts with Improved Mechanical Properties in Selective Laser Sintering”. In: Journal of Manufacturing Processes 57 (2020), pp. 828–846. issn: 1526-6125.

Honors & awards

  • National Academy of Engineering 2023 Frontiers of Engineering Alumnus
  • National Science Foundation CAREER Award, 2018
  • SME Outstanding Young Manufacturing Engineer Award
  • Mechatronic Systems Outstanding Young Researcher Award, International Federation of Automatic Control (IFAC) Technical Committee on Mechatronic Systems, 2022
  • Best Paper Award, ISCIE/ASME International Symposium on Flexible Automation, 2018
  • Best Vibrations Paper Award, ASME Dynamic Systems and Control Division, 2017
  • Faculty Mentor for Best Student Paper on Robotics, ASME Dynamic Systems and Control Division, 2019
  • Faculty Mentor for Best Student Paper on Mechatronics, ASME Dynamic Systems and Control Division, 2018
  • UTC Institute for Advanced Systems Engineering Breakthrough Award, 2016
  • Young Investigator Award from ISCIE / ASME International Symposium on Flexible Automation, 2014