Dongyang Fu

About

Dongyang Fu is a leading researcher in robotics, neural dynamics, and numerical computation, with a focus on solving time-variant equations for real-time control applications. His major contributions include pioneering work on recurrent neural networks (RNNs) for solving the time-variant generalized Sylvester equation, a formulation that unifies the Sylvester, Lyapunov, and Stein equations, with applications to robot kinematics and acoustic source localization (174 citations). Fu has also advanced noise-suppressing algorithms for robotic control, such as the noise-suppressing Newton algorithm for kinematic control of redundant robots (23 citations) and the modified Newton integration algorithm with noise tolerance for robotics (18 citations). His work on two neural dynamics approaches for time-varying nonlinear equations (32 citations) and the noise-suppressing Newton-Raphson iteration for Lyapunov equations (22 citations) further demonstrates his impact. Notably, Fu’s recent research on data-driven motion-force control for acceleration minimization (2023) and zeroing feedback gradient-based neural dynamics for quadratic programming (2024) highlights his ongoing innovation in robust, noise-tolerant robotic systems. With over 275 total citations, Fu’s work is essential for students and researchers in robotics, control theory, and computational mathematics.

Research Focus

Key Achievements

Top Papers

1. 1
   RNN for Solving Time-Variant Generalized Sylvester Equation With Applications to Robots and Acoustic Source Localization

   174 citations · 2020
2. 2
   Two neural dynamics approaches for computing system of time-varying nonlinear equations

   32 citations · 2020
3. 3
   Noise-Suppressing Newton Algorithm for Kinematic Control of Robots

   23 citations · 2019
4. 4
   A noise-suppressing Newton-Raphson iteration algorithm for solving the time-varying Lyapunov equation and robotic tracking problems

   22 citations · 2020
5. 5
   Modified Newton Integration Algorithm With Noise Tolerance Applied to Robotics

   18 citations · 2021
6. 6
   Modified Newton integration algorithm with noise suppression for online dynamic nonlinear optimization

   12 citations · 2020
7. 7
   Data-driven Motion-force Control for Acceleration Minimization of Robots

   4 citations · 2023
8. 8
   A zeroing feedback gradient-based neural dynamics model for solving dynamic quadratic programming problems with linear equation constraints in finite time

   2 citations · 2024

Key Collaborators

Contact & Links