Motion Generation with Smart Materials: Scalable Solutions to Modeling, Control, and Design
Recent years have seen significant advances in motion generation for microelectromechanical systems (MEMS) and robots using smart materials as the actuation mechanisms. Thanks to their inherent material properties, smart materials enable movement with precision; compared with electric motors, smart material actuators are more compact, have fewer moving parts, and are often more compliant. On the other hand, these actuators demonstrate complex dynamics and nonlinearities, such as hysteresis, presenting challenges in their design and control. As smart materials become widely adopted with increasing complexity, it is imperative to develop efficient and scalable solutions for understanding and capturing intrinsic material behaviors, enabling control design for guaranteed performance, and advancing material and device designs to meet requirements in versatile applications.
In this talk, I will discuss our effort in addressing these needs, taking two types of novel smart material actuators as case studies: MEMS actuators based on vanadium dioxide, and artificial muscles made of conductive nylon threads. The proposed modeling, design, and control methods are experimentally verified on MEMS and robotic prototypes. I will also discuss my future research plans on advancing smart material-enabled micro-scale systems, soft robots, and assistive devices.
Biosketch | Jun Zhang is a postdoctoral scholar in Electrical and Computer Engineering at the University of California, San Diego. He received the B.S. degree in Automation from the University of Science and Technology of China, Hefei, China, in 2011, and the Ph.D. degree in Electrical and Computer Engineering from Michigan State University in 2015. His research interests lie in the intersection of control theory, robotics, smart materials, and artificial muscles. He was the recipient of the Student Best Paper Competition Award at the ASME Conference on Smart Materials, Adaptive Structures, and Intelligent Systems (SMASIS 2012), and the Best Conference Paper in Application Award at the ASME Dynamic Systems and Control Conference (DSCC 2013), and was named the Electrical Engineering Outstanding Graduate Student at Michigan State University for 2014–2015.