Tengda Wang
Papers
1
Total Citations
17
H-Index
1
About
Tengda Wang is an emerging researcher in nonlinear control systems, with a focus on sliding mode control, event-triggered mechanisms, and adaptive dynamics under complex constraints. Their most-cited work, "Zero-sum game-based dynamic self-triggered sliding mode control for unknown nonlinear systems with asymmetric input constraints" (2025, 17 citations), introduces a novel framework that combines game-theoretic optimization with self-triggered control to address the challenges of unknown system dynamics and asymmetric input constraints. This contribution is particularly significant for applications in robotics, aerospace, and autonomous systems, where real-time decision-making under uncertainty is critical. By integrating dynamic triggering and sliding mode techniques, Wang’s approach reduces computational load while maintaining robustness—a key advancement for resource-limited platforms. Though early in their career, Wang’s work has already attracted attention for its theoretical rigor and practical relevance, bridging gaps between optimal control, game theory, and nonlinear system stability. Their research holds promise for advancing intelligent control in safety-critical environments, marking them as a rising voice in the field.
Research Focus
Key Achievements
Top Papers
- 1