Zhaoguo Jiang
Papers
3
Total Citations
32
H-Index
3
About
Zhaoguo Jiang is a researcher focused on advancing the control and modeling of smart material actuators, particularly dielectric electro-active polymers (DEAPs) for bio-inspired robotics. His work addresses critical challenges in actuator precision, including hysteresis, creep, and dynamic nonlinearities. Jiang’s major contributions include developing an intelligent feedforward hysteresis compensation and tracking control system for DEAP actuators, which achieved 18 citations and represents a key step toward reliable robotic motion. He also pioneered an empirical mode decomposition-based long short-term memory network to model dynamic, rate-dependent hysteresis, and proposed a dynamic compensation method for load-dependent creep—both published in 2021 with 7 citations each. These innovations tackle the asymmetrical, time-varying behaviors that limit DEAP actuator performance in real-world applications. By integrating machine learning with physics-based modeling, Jiang’s research bridges the gap between smart material theory and practical robotic control. His work is highly relevant for students and researchers in soft robotics, mechatronics, and intelligent systems, offering foundational techniques for enhancing actuator accuracy and adaptability in autonomous and bio-inspired machines.
Research Focus
Key Achievements
Top Papers
- 1
- 2
- 3