Papers
15
Total Citations
437
H-Index
10
About
Dan Zhang is a prolific researcher specializing in robust control systems, robotics, and autonomous systems, with a particular focus on sliding mode control, disturbance observers, and multi-robot coordination. His work addresses some of the most pressing challenges in modern robotics, including trajectory tracking under uncertainty, fault-tolerant control, and real-time obstacle avoidance. Zhang's most impactful contribution, "A Novel Disturbance Observer Based Fixed-Time Sliding Mode Control for Robotic Manipulators" (2024), has already garnered 142 citations, demonstrating the immediate relevance of his fixed-time convergence frameworks to the robotics community. His research consistently tackles real-world complexities such as actuator faults, input saturation, external disturbances, and adversarial cyber-attacks, developing adaptive and neural network-enhanced control strategies that maintain guaranteed performance under these conditions. Beyond manipulator control, Zhang has made significant contributions to mobile robotics, including formation control of nonholonomic robot systems, wheeled mobile robot posture control with skidding compensation, and medical robotics through improved needle-steering observers. His work on distributed predictive and primal-dual neural network approaches reflects a systems-level vision for multi-agent coordination. With a body of work spanning foundational theory to applied robotics, Dan Zhang has established himself as a significant voice in intelligent, resilient control systems research.
Research Focus
Key Achievements
Top Papers
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- 7Distributed Leader-follower Formation Control of Nonholonomic Mobile Robots14 citations · 2019
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- 10An Improved High-Gain Observer Design for Flexible Needle Steering10 citations · 2023