Fei Chang
Papers
1
Total Citations
2
H-Index
1
About
Dr. Fei Chang is a robotics researcher specializing in advanced control systems for autonomous mobile platforms, with a particular focus on two-wheeled self-balancing robots. Their most notable contribution is the development of a novel control design methodology that combines tensor product (TP) model transformation with parallel distributed compensator (PDC) control, applied to GOOGOL’s mobile two-wheeled self-balancing robot. This work, published in 2021, addresses critical challenges in real-time control by introducing a non-fixed-time step sampling method, which mitigates the exponential computational burden typically associated with high-dimensional linear variable parameter (LPV) models. While the paper has garnered 2 citations to date, its significance lies in identifying and proposing a solution to a fundamental limitation in TP-based control—a problem that has constrained the practical deployment of these methods in complex, high-dimensional systems. Dr. Chang’s research sits at the intersection of control theory and robotics, offering pathways toward more computationally efficient and scalable control architectures for next-generation autonomous vehicles and mobile robots. Their work is particularly relevant for researchers tackling real-time control in resource-constrained embedded systems.
Research Focus
Key Achievements
Top Papers
- 1