Shaofan Guo

About

Shaofan Guo is a leading researcher in advanced control systems, with a primary focus on teleoperation robotics, adaptive control, and nonlinear system stability. Their work addresses critical challenges in teleoperation, particularly synchronization tracking under input saturation, output error constraints, and time delays—issues vital for applications in remote surgery, hazardous environment manipulation, and space robotics. Guo’s major contributions include developing adaptive practical predefined-time control schemes that guarantee system convergence within a user-specified timeframe, even under uncertainties and actuator faults. Their 2023 paper on predefined-time control for teleoperation systems has garnered 64 citations, reflecting its significance in the field. Additionally, Guo has pioneered fixed-time control methods that handle time-varying output constraints and communication delays, as seen in their 2022 work (10 citations), and has advanced fault-tolerant control using multi-approximator architectures to ensure safety under deferred constraints (9 citations). By integrating backstepping design, neural learning, and predefined-time theory, Guo’s research pushes the boundaries of reliable, high-performance teleoperation, offering practical solutions for real-world robotic systems where precision and timeliness are paramount.

Research Focus

Key Achievements

Top Papers

1. 1
   Adaptive Practical Predefined-Time Control for Uncertain Teleoperation Systems With Input Saturation and Output Error Constraints

   64 citations · 2023
2. 2
   Adaptive neural learning fixed‐time control for uncertain teleoperation system with time‐delay and time‐varying output constraints

   10 citations · 2022
3. 3
   Multiapproximator-based adaptive fault-tolerant control for teleoperation systems with deferred asymmetric time-varying output constraints

   9 citations · 2023

Key Collaborators

Contact & Links