Xianbao Jiang
Papers
1
Total Citations
11
H-Index
1
About
Xianbao Jiang is a researcher specializing in teleoperation systems, adaptive control, and neural network-based modeling. His work focuses on enhancing the precision and reliability of remote robotic manipulation, particularly through the development of novel force observation techniques. Jiang’s most cited paper, "An adaptive sparse general regression neural network-based force observer for teleoperation system" (2022), introduces a groundbreaking approach that combines sparse regression with neural networks to estimate external forces in real-time, significantly improving haptic feedback and operator control in teleoperation. This contribution addresses critical challenges in human-robot interaction, such as stability under varying environmental conditions. With 11 citations to date, this work has already influenced subsequent studies in adaptive control and sensorless force estimation. Jiang’s research bridges theoretical advancements in machine learning with practical applications in robotics, making his work highly relevant for engineers and researchers developing next-generation teleoperation systems for surgery, hazardous material handling, and remote exploration. His achievements underscore a commitment to advancing autonomous and semi-autonomous robotic systems through intelligent, data-driven solutions.
Research Focus
Key Achievements
Top Papers
- 1