Papers
7
Total Citations
72
H-Index
5
About
Sze Zheng Yong is a pioneering researcher at the intersection of control theory, robotics, and cyber-physical systems, whose work fundamentally addresses how autonomous systems can infer the hidden intents of other agents—whether human drivers, robots, or malicious attackers. His most influential contributions center on **active model discrimination**, a framework he developed to design optimal input signals that can distinguish among multiple possible system behaviors. This work has profound implications for **fraud detection in smart buildings** (19 citations), **intention-aware autonomous vehicles** (16 citations), and **fault diagnosis** in safety-critical systems. Yong’s approach is distinguished by its mathematical rigor: he has extended these techniques from affine to **nonlinear models** through affine abstraction (14 citations), enabling broader applicability. Beyond discrimination, he has made notable contributions to **neuromorphic vision sensors** for stabilizing linear systems (12 citations) and to **tethered robot motion planning** on extreme terrains, including a winding-constrained hybrid A* algorithm (6 citations). His recent work on **set-based intent-expressive trajectory planning** (3 citations) represents a novel, deterministic alternative to stochastic methods. Yong’s research is characterized by its practical motivation—addressing real-world challenges in robotics, security, and autonomous driving—while advancing fundamental control-theoretic tools for model discrimination and intent estimation.
Research Focus
Key Achievements
Top Papers
- 1
- 2
- 3Input Design for Nonlinear Model Discrimination via Affine Abstraction14 citations · 2018
- 4
- 5
- 6Set-Based Intent-Expressive Trajectory Planning and Intent Estimation3 citations · 2022
- 7Stability of Tethered Ground Robots on Extreme Terrains2 citations · 2024