Papers
20
Total Citations
208
H-Index
8
About
Jundong Wu is a prominent researcher specializing in soft robotics, smart actuator modeling, and advanced control systems, with particular expertise in dielectric elastomer actuators (DEAs) and liquid crystal elastomer (LCE) actuators. His work sits at the intersection of materials science, nonlinear dynamics, and intelligent control, addressing some of the most challenging problems in next-generation soft robotic systems. Wu's most significant contributions lie in developing sophisticated modeling and control frameworks for inherently nonlinear smart materials. His application of GRU neural networks for inverse dynamics modeling of conical DEAs and his use of fractional calculus for actuator modeling demonstrate a creative command of both classical and data-driven approaches. His research on self-sensing motion control using NARX neural networks and iterative learning control represents a notable advancement in integrating actuation and sensing within a single device. He has also made important strides in photo-responsive LCE actuator modeling, addressing complex hysteretic nonlinearities critical for light-driven soft robots. With over 175 cumulative citations across his top works—including a well-received book on underactuated manipulator control—Wu has established himself as a valuable contributor to the soft robotics and intelligent control communities. His research is particularly relevant for engineers and scientists developing flexible, biomimetic robotic systems.
Research Focus
Key Achievements
Top Papers
- 1
- 2Modeling of photo-responsive liquid crystal elastomer actuators35 citations · 2021
- 3
- 4
- 5
- 6
- 7
- 8
- 9
- 10Control of Underactuated Manipulators6 citations · 2023