Bingyang Chen
Papers
1
Total Citations
4
H-Index
1
About
Bingyang Chen is a researcher whose work lies at the intersection of advanced control theory and robotics, with a particular focus on the stability and trajectory control of complex, underactuated systems. His major contribution is the development of a novel sliding-mode control method augmented with a broad learning system (BLS-SMC), specifically designed for the challenging task of balancing and maneuvering an Inverse-Atlas ball-riding robot (IASBRR). This robot, which relies on three omnidirectional wheels, presents significant uncertainties and nonlinear dynamics. Chen’s innovative approach integrates the robustness of sliding-mode control with the adaptive, data-driven capabilities of a broad learning network, enabling precise station keeping and trajectory tracking even under uncertain conditions. While his most cited paper, published in 2019, has garnered 4 citations, its conceptual novelty is notable for pushing the boundaries of how machine learning can be embedded into classical control frameworks for real-time robotic applications. Chen’s work is particularly relevant for researchers in mechatronics and nonlinear control, offering a practical blueprint for stabilizing inherently unstable robotic platforms.
Research Focus
Key Achievements
Top Papers
- 1