This week’s faculty spotlight is on Professor Xu Chen, who is an assistant professor in the Department of Mechanical Engineering. Dr. Chen received his M.S. and Ph.D. degrees in Mechanical Engineering from the University of California, Berkeley in 2010 and 2013, respectively, and his Bachelor’s degree from Tsinghua University, China in 2008.  He is a recipient of the National Science Foundation CAREER award, the Young Investigator Award and the Best Paper Award from ISCIE / ASME International Symposium on Flexible Automation, the 2017 Best Vibrations Paper Award from the ASME Dynamic Systems and Control Division, the 2017 UConn University Teaching Fellow Award Nominee, and the 2012 Chinese Government Award for Outstanding Students Abroad.

 

Professor Chen is the principle investigator for the Machine, Automation and Control Systems Laboratory (MACS) through the Department of Mechanical Engineering. The overall research goal of his lab is to seek better understanding and engineering of the systematic interplay between data, systems and controls in machines and automation processes. For instance, fast situational awareness and agile response is imperative to advancing system operation in this information age. To reach such capabilities, Prof. Chen’s team exploits approaches to reliably and quickly combine all data from heterogeneous sources in a feedback control system. An example of such is a project conducted from Dr. Chen’s smart manufacturing research called “Model-Based Sparse Information Recovery by Collaborative Sensor Management”. This project provides a novel approach to collect dense information from a group of collaborative sensors at a significantly reduced computation burden and in real time. The result is particularly impactful for applications such as imagining-based automation, where vision data take time to collect and complex elaborations must be performed to extract information from the raw data. More broadly, this work relates to the overarching challenge of making full use of data to infer and respond to fast evolving situations in decentralized environments, and provides a pathway to better integrate multiple data-intensive sources.

 

During the summer of 2018, Dr. Chen and his laboratory team traveled to three flagship conferences in the fields of controls, automation, and 3D printing: The American Control Conference at Milwaukee in June, the International Symposium on Flexible Automation at Kanazawa, Japan in July, and the Annual international Solid Freedom Fabrication at Austin, Texas in August. The three published papers from Prof. Chen’s team discussed smart controls approaches for critically needed quality assurance of additive manufacturing (AM), a nascent manufacturing technology that offers untapped potential in a wide range of products for the energy, aerospace, automotive, healthcare and biomedical industries. In particular, the focused powder bed fusion process is increasingly preferred in applications ranging from advanced jet-engine components to custom-designed medical implants. Prof. Chen’s research looks into the convoluted thermomechanical interactions in the multi-physics multi-scale manufacturing process, and has been generating award-winning, internationally recognized results to enable substantially higher accuracy and greater reproducibility in AM. For instance, the recent paper titled “Synthesis and Analysis of Multirate Repetitive Control for Fractional-Order Periodic Disturbance Rejection in Powder Bed Fusion” was featured in the proceedings of the 2018 International Symposium on Flexible Automation, where this paper written by PhD Student, Dan Wang, and Dr. Chen, won the “Best Paper Award In Theory”.

 

This week’s UTC-IASE faculty spotlight is on Dr. Ashwin Dani, who is an assistant professor in the Department of Electrical and Computer Engineering. He received his B.S in Mechanical Engineering from the University of Pune in India, and then went on to receive a Ph.D in Mechanical and Aerospace Engineering from the University of Florida. He began his career in academia as a post-doctoral research associate at the University of Illinois at Urbana-Champaign in 2011, and then moved to the University of Connecticut in 2013 and currently works as an assistant professor.

 

Dr. Dani is the PI for the Robotics and Controls Lab, which focuses on solving various estimation and control challenges in engineering domains such as robotics, automation, industrial and biomedical applications. His research falls into areas such as (1) Model building from data using machine learning, (2) Human-Robot collaboration and safety issues in manufacturing environments, (2) GPS-denied navigation of unmanned aerial systems and improved autonomy, (4) sensor data fusion, and (5) estimation and control for neuro-prosthesis. He has also done previous work in areas such as estimation and control theory, robotics, autonomous navigation, localization and mapping, and vision-based control.

 

This summer, Dr. Dani attending the American Control Conference in Milwaukee, where a paper titled “Learning Stable Nonlinear Dynamical Systems with External Inputs using Gaussian Mixture Models” was presented by the UTC-IASE Fellow Iman Salehi. Iman Salehi, a UTC-Fellowship graduate student, worked under the counsel of Dr. Dani this summer. Salehi’s project is titled “Physics Informed Machine Learning Project”, which is researching how to develop models for heat exchangers using machine learning that embeds dynamical system properties. The efforts in this project bring a paradigmatic change to how model building using machine learning is looked at to include physical properties of the system. This paper presented a data-driven modeling method with convergence guarantees embedded in machine learning. This paper is closely related to the “Physics-Informed Machine Learning Project”.

 

Professor Dani worked with UTC this summer on a project titled “Physics Informed Machine Learning” and “Wire Harness Assembly using Robots”, with a goal to develop methodologies and architectures for data-driven model learning, containing dynamical system properties coming from physics of the system embedded in machine learning. In terms of research publications, Dr. Dani and his lab worked on extending the conference manuscripts presented over the summer, to journal manuscripts, for projects  “Physics Informed Machine Learning”.

 

UTC IASE faculty member, Dr. Abhishek Dutta, was recently highlighted by the university, in recognition of his work on biological control systems and bio-robotics. Dr. Dutta, who is an assistant professor of Electrical and Computer Engineering, is using his specialization in control systems, design and optimization and cybernetics, to build a hardwired biological insect that can be used to precisely control the insect’s motion.  Since then, the “UConn neuro controller” has been trending in hundreds of news outlets worldwide engaging as many discussions on social media.

The microcircuit is strapped to the roach, and then interfaces with wired micro-electrodes that connect into the antenna lobes. The signal is transmitted through a wireless device in the circuit and a receiver allows for the motion to be controlled through a ground station. The microcircuit sends tiny electrical currents through the wires into the neural tissue in the antenna lobe. Once the brain receives these impulses, it makes the insect believe that there is an obstacle away. Therefore, if an appropriate signal is received by the left antenna lobe, the insect will move to the right and vice-versa.

Professor Dutta is using the Madagascar Hissing Cockroach as the organism under study, where he has built a microcircuit that wires directly into the brain of the roach and uses the circuit as a neuro controller. The circuit incorporates a 9-axis inertial measurement unit that can detect the roach’s six degrees of free motion, its linear and rotational acceleration, as well as its compass heading. Other features that help with the performance of the circuit is a sensor to detect the ambient surrounding temperature, which will help map the correlation between the environmental conditions and the motion of the roach.

Although a couple of similar systems have been developed previously, this microcircuit stands out because it offers a multi-faceted approach for stimulating the nerve tissue, real-time feedback of the insect’s response to the electrical stimuli, and a greater degree of control of the insect’s motion, resulting in a more informed and precise control system. As the microcontroller and built-in potentiometer work in tandem, the operator is able to vary the output voltage, frequency, and cycle of stimuli delivered, which helps to produce a more robust response, while not causing damage to the roach.

This work shows great promise, with the possibility of human applications in the future. Microcircuit and micro-control systems, such as these, are the first step in developing  larger-scale controllers and circuits that can be used in industries like healthcare, automotive, and artificial intelligence. One proposed use is to use this technology to help control motor functions in organisms with spinal cord injury or paraplegic organisms. “Our microcircuit provides a sophisticated system for acquiring real-time data on an insect’s heading and acceleration, which allows us to extrapolate its trajectory,” says Dr. Dutta. “We believe this advanced closed loop, model-based system provides better control for precision maneuvering and overcomes some of the technical limitations currently plaguing today’s micro robots”. Dr. Dutta states that much more research is needed, although the current technology has great promise to lead to a new generation of products with even larger scale applications.