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Xu Chen

Faculty Photo

Associate Professor
Mechanical Engineering



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. Before joining UW, he was at the University of Connecticut (UConn) as an assistant professor of mechanical engineering, in affiliation with materials science and advanced systems engineering.

Dr. Chen pursues a lifelong 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 into sensing, actuation, and energy transformation that facilitate novel machines and manufacturing processes: e.g., advisor robots for automated inspection in 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 three 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 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 Publicity and Local Arrangements Chairs of the 2020 and the 2023 IEEE/ASME International Conferences on Advanced Intelligent Mechatronics, and Exhibits Chair of the 2021 IEEE American Control Conference. He served the ASME Dynamic Systems and Control (DSC) Division in roles including Chair of the Vibration Technical Committee, News Editor of the DSC Magazine, Editor of the DSC Newsletter, and Student and Young Members Chair of the 2016 and the 2020 ASME DSC Conferences.


  • 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. 

Sensing and Control under Constrained Information Feedback

Since the invention of microprocessors, fast and regularly sampled data has been the dogma for building ubiquitous realtime systems in applications from healthcare devices to manufacturing and transportation platforms. Converging trends toward decentralized and data-rich sensing, however, challenge the golden theoretical framework. In a world increasingly involving connected systems and elaborated measurements from such sources as sound, imaging, and videos, how to make full use of data to infer and respond to fast-evolving situations has remained poorly understood. Not only do communication constraints and long data processing times obstruct fast information assimilation, the asynchronous nature of multi-channel information flow also hinders consistent realtime operations. These monumental roadblocks handicap the potential for smart engineered systems to ignite the next evolution of autonomous platforms, and have led to life-threatening system failures and even fatalities in manufacturing and transportation. Results from Prof. Chen shows, however, the possibility of integrating multiple slow, asynchronous sensors to amply information feedback at a nonlinear rate.

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 (2019). in production.
  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. H. Xiao, T. Jiang, and X. Chen. “Rejecting Fast Narrow-band Disturbances with Slow Sensor Feedback for Quality Beam Steering in Selective Laser Sintering”. In: Mechatronics 56 (2018), pp. 166–174. issn: 0957-4158.
  5. 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.
  6. 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.
  7. 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.
  8. 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.
  9. 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.
  10. 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.
  11. 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.
  12. X. Chen, T. Jiang, D. Wang, and H. Xiao. “Realtime Control-Oriented Modeling and Disturbance Parameterization for Smart and Reliable Powder Bed Fusion Additive Manufacturing”. In: Annual International Solid Freeform Fabrication Symposium. 2018.
  13. D. Wang and X. Chen. “Synthesis and Analysis of Multirate Repetitive Control for Fractional-order Periodic Disturbance Rejection in Powder Bed Fusion”. In: Proceedings of 2018 International Symposium on Flexible Automation. Best Paper Award. July 2018.
  14. T. Jiang, H. Xiao, and X. Chen. “An Inverse-Free Disturbance Observer for Adaptive Narrow-Band Disturbance Rejection with Application to Selective Laser Sintering”. In: Proceedings of ASME Dynamic Systems and Control Conference. Best Vibration Paper Award, ASME Dynamic Systems and Control Division. 2017.
  15. H. Xiao and X. Chen. “Multi-band beyond-Nyquist Disturbance Rejection on a Galvanometer Scanner System”. In: Proceedings of IEEE International Conference on Advanced Intelligent Mechatronics, July 3- 7 2017, Munich, Germany. Best Student Paper on Mechatronics Award, ASME Dynamic Systems and Control Division. 2017, pp. 1700–1705.
  16. 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

  • Faculty Mentor for Best Student Paper on Robotics, , ASME Dynamic Systems and Control Division, 2019
  • National Science Foundation CAREER Award, 2018
  • Best Paper Award, ISCIE/ASME International Symposium on Flexible Automation, 2018
  • Faculty Mentor for Best Student Paper on Mechatronics, ASME Dynamic Systems and Control Division, 2018
  • Best Vibrations Paper Award, ASME Dynamic Systems and Control Division, 2017
  • University Teaching Fellow Award Nominee, University of Connecticut, 2017
  • Research Excellence Program, University of Connecticut, 2017
  • UTC Institute for Advanced Systems Engineering Breakthrough Award, 2016
  • Young Investigator Award from ISCIE / ASME International Symposium on Flexible Automation, 2014