Yu‐Ming Chu

About

Yu-Ming Chu is a leading researcher in the integration of computational intelligence, multi-criteria decision-making (MCDM), and advanced control systems, with a particular focus on robotics and automation. His major contributions lie in developing hybrid fuzzy set models—such as the q-rung orthopair m-polar fuzzy sets—to tackle complex, uncertain decision environments in robotic agri-farming and industrial robot selection. Chu’s work on the hybrid BW-EDAS MCDM methodology, which has garnered 80 citations, provides a robust framework for optimal industrial robot selection, addressing the challenge of matching diverse robot specifications to specific applications. In control theory, he has advanced robust nonsingular sliding mode control for non-holonomic spherical robots, integrating recurrent neural networks to handle input saturation and stabilization, a contribution cited 52 times. His recent research on global fixed-time event-triggered control for stochastic nonlinear systems with full state constraints (20 citations) pushes the boundaries of safety and efficiency in autonomous systems. With over 190 citations across his top papers, Chu’s work is instrumental in bridging fuzzy logic, decision science, and nonlinear control, offering practical solutions for real-world robotics and automation challenges.

Research Focus

Key Achievements

Top Papers

1. 1
   Hybrid BW-EDAS MCDM methodology for optimal industrial robot selection

   80 citations · 2021
2. 2
   Recurrent Neural Network-Based Robust Nonsingular Sliding Mode Control With Input Saturation for a Non-Holonomic Spherical Robot

   52 citations · 2020
3. 3
   Multi-criteria decision making in robotic agri-farming with q-rung orthopair m-polar fuzzy sets

   40 citations · 2021
4. 4
   Global fixed-time event-triggered control for stochastic nonlinear systems with full state constraints

   20 citations · 2023

Key Collaborators

Contact & Links