Archives: People

Xudong Chen is an Assistant Professor in the Department of Electrical, Computer, and Energy Engineering at the University of Colorado Boulder. Prior to that, he was a postdoctoral fellow in the Coordinated Science Laboratory at the University of Illinois, Urbana-Champaign. He obtained the B.S. degree in Electronics Engineering from Tsinghua University, China, in 2009, and the Ph.D. degree in Electrical Engineering from Harvard University, Massachusetts, in 2014. He is an awardee of the 2020 Air Force Young Investigator Program, a recipient of the 2021 NSF CAREER award, and the recipient of the 2021 Donald P. Eckman award. His current research interests are in the area of control theory, stochastic processes, optimization, game theory and their applications in modeling, analysis, control, and estimation of large-scale complex systems.

Structure Theory for Nonholonomic Ensemble Systems

Ensemble control deals with the problem of using a common control input to simultaneously steer a large population (in the limit, a continuum) of individual control systems.  In this talk, we address a fundamental problem in ensemble control theory, namely, system controllability. A key factor in determining controllability of an ensemble system is its underlying parameterization space. Roughly speaking, the bigger the parameterization space is, the more difficult one can control the ensemble. Over the past two decades, significant progress has been made for understanding controllability of ensemble systems over one-dimensional parameterization spaces, yet little is known when the dimensions are greater than one. A major focus of this talk is to present recent advances in controllability of ensemble systems whose parameterization spaces are multi-dimensional. We will consider two classes of ensemble systems, namely, ensembles of linear control systems and ensembles of control-affine systems. We will first show that linear ensemble systems are problematic if their parameterization spaces are greater than one and, then, show how to resolve this controllability issue by using a special class of control-affine ensembles whose control vector fields are equipped with a fine structure.

Draguna Vrabie is a Chief Data Scientist in the Data Sciences and Machine Intelligence Group at Pacific Northwest National Laboratory. She serves as Team Lead for the Autonomous Learning and Reasoning Team and Thrust Lead for the Data Model Convergence Initiative. Before joining PNNL in 2015, she was a senior scientist in the Control Systems Group at United Technologies Research Center, East Hartford, Connecticut, from 2010-2015. Vrabie received a Ph.D. in Electrical Engineering for her work on adaptive optimal control by reinforcement learning principles from the University of Texas at Arlington. She received Dipl. Ing. and M.S. degrees in Automatic Control and Computer Engineering from the Gheorghe Asachi Technical University in Iasi, Romania. Her work focuses on the design of adaptive and predictive control systems. Her current research is on control and deep learning. She applies her expertise to high-performance cyber-physical energy systems and autonomous scientific discovery. Vrabie co-authored two books on optimal control, reinforcement learning, and differential games and has published over seventy peer-reviewed journal and conference papers. Her work was recognized with Best Paper awards and two corporate awards for outstanding achievement and operational excellence. Vrabie holds seven patents, and in 2021 she received an R&D100 Award. She is a member of the IEEE.

Control design based on deep learning

Abstract: Control systems with learning abilities could cost-effectively address societal issues like energy reliability, decarbonization, climate security and enable autonomous scientific discovery. Recent investigations focus on longstanding challenges such as robustness, uncertainty, and safety of complex engineered systems. But most importantly, innovation in deep learning methods, tools, and technology offers an unprecedented opportunity to transform the control engineering practice and bring much excitement to control systems theory research. In this talk, I will introduce recent results in modeling dynamic systems with deep learning representations that embed domain knowledge. I will also discuss differentiable predictive control, a data-driven approach that uses physics-informed deep learning representations to synthesize predictive control policies. I’ll illustrate the concepts with examples from various engineering applications. I’ll close by considering the implications of differentiable programming on the broader control systems context.

Dr. Xiaobo Tan is an MSU Foundation Professor and the Richard M. Hong Endowed Chair in Electrical and Computer Engineering at Michigan State University (MSU). He received his bachelor’s and master’s degrees in automatic control from Tsinghua University, Beijing, China, in 1995, 1998, respectively, and his Ph.D. in electrical and computer engineering (ECE) from the University of Maryland in 2002. His research interests include bio-inspired robots, soft sensors and actuators, and modeling and control of systems with hysteresis. In particular, his group has developed and field-tested autonomous underwater and surface robots for mobile sensing applications. He has published over 300 papers and been awarded four US patents in these areas.

Dr. Tan is a Fellow of IEEE and ASME. He is a recipient of the NSF CAREER Award (2006), MSU Teacher-Scholar Award (2010), MSU College of Engineering Withrow Distinguished Scholar Award (2018), Distinguished Alumni Award from the ECE Department at University of Maryland (2018), and multiple best paper awards. He is currently a Senior Editor for IEEE/ASME Transactions on Mechatronics and serves on the Executive Committee of the ASME Dynamic Systems and Control Division. Dr. Tan is keen to integrate his research with educational and outreach activities, and has served as Director of an NSF-funded Research Experiences for Teachers (RET) Site program at MSU from 2009 – 2016 and Curator of a robotic fish exhibit at MSU Museum in 2016-2017.

Fish and Robot: Bio-inspiration, Underwater Sensing, and the Role of Dynamics and Control

Evolution over the course of 500 million years has endowed fish with superior swimming and sensory capabilities in water. This has not only captivated the interest of biologists, but also spurred the development of underwater machines aiming to emulate fish’s locomotion and sensing marvels. In this talk I will first discuss efforts in developing hydrodynamic sensing systems inspired by lateral lines, the flow-sensing organ of fish. I will then illustrate the important role played by advanced modeling and control tools in optimizing robotic fish’s locomotion performance. I will further introduce gliding robotic fish, a new class of robotic fish that incorporates gliding to boost locomotion energy-efficiency, and discuss its application to autonomous underwater sensing. In one example, the unique spiral dynamics of gliding robotic fish is exploited in sampling the distribution of harmful algae along water columns. In another example, a network of gliding robotic fish is proposed for tracking the movement of invasive fish species with acoustic telemetry, and we show how distributed filtering algorithms can be used to localize the moving target. Both examples will be supported with results from field experiments.

Frank Doyle is the John A. Paulson Dean of the Paulson School of Engineering and Applied Sciences at Harvard University, where he also is the John A. & Elizabeth S. Armstrong Professor. Prior to that he was the Mellichamp Professor at UC Santa Barbara, where he was the Chair of the Department of Chemical Engineering, the Director of the UCSB/MIT/Caltech Institute for Collaborative Biotechnologies, and the Associate Dean for Research in the College of Engineering. He received a B.S.E. degree from Princeton, C.P.G.S. from Cambridge, and Ph.D. from Caltech, all in Chemical Engineering. He has been recognized as a Fellow of multiple professional organizations including: IEEE, IFAC, AIMBE, AIChE and the AAAS. He was the President for the IEEE Control Systems Society in 2015 and was the Vice President and Chair of the Technical Board for the International Federation of Automatic Control from 2014 to 2017. In 2015 he received the Control Engineering Practice Award from AACC and in 2020 received the Industrial Achievement Award from IFAC; both for the development of the artificial pancreas. He is an elected member of the National Academy of Medicine (2016), the National Academy of Inventors (2020), and the National Academy of Engineering (2021). His research interests are in systems biology, network science, modeling and analysis of circadian rhythms, and drug delivery for diabetes. In his spare time, he enjoys hiking with his family, genealogical research, and is a certified soccer referee at the adult and collegiate level.

Translating Control Technology for Personalized Medicine

The future of healthcare will involve personalized medical therapies for individuals. In applications involving the delivery of a drug (for example, insulin), such personalization can be achieved through the use of tailored feedback control strategies. For close to 30 years, our research group has collaborated with medical experts on the design of algorithms for safe and effective insulin delivery for individuals with Type 1 diabetes mellitus (T1DM). T1DM is a chronic autoimmune disease affecting approximately 35 million individuals world-wide, with associated annual healthcare costs in the US estimated to be approximately $15 billion. Over the years, there have been remarkable innovations in glucose measurement technology, insulin pump design, and personalized control algorithms. Over the last 5 years, multiple commercial closed-loop devices have entered the market, thus delivering the so-called “artificial pancreas” to individuals with T1DM. In this talk, I will outline the difficulties inherent in controlling physiological variables, the challenges with regulatory approval of such devices, and will describe several control systems engineering algorithms we have tested in clinical and outpatient settings for the artificial pancreas. I will describe our work in creating an embedded version of our MPC algorithm to enable a portable implementation in a medical IoT framework and will highlight some of the open challenges for automated insulin delivery. I’ll close by sharing other medical examples where feedback algorithms could provide transformational advances in personalized medicine, including chronotherapy.