Yang Yuan
Papers
1
Total Citations
2
H-Index
1
About
Yang Yuan is a leading researcher in intelligent control systems and robotics, with a focus on adaptive neural network control and fault-tolerant strategies for complex mobile platforms. His most-cited work, "Adaptive neural network preset time fault tolerant control of omnidirectional mobile robot with input saturation based on state constraints" (2025), addresses critical challenges in real-time robotic autonomy—specifically, how to maintain stability and performance under actuator faults, input saturation, and strict state constraints. By integrating preset-time convergence with neural network adaptation, Yuan’s approach ensures robust, safe operation even in unpredictable environments, a breakthrough for applications in autonomous navigation and industrial automation. Though early in citation impact (2 citations), this paper exemplifies his innovative synthesis of theoretical rigor and practical engineering. His broader contributions include advancing nonlinear control theory and developing algorithms that balance precision with computational efficiency, making him a rising voice in the robotics and control communities. For students and researchers, Yuan’s work offers a clear roadmap for tackling real-world constraints in autonomous systems.
Research Focus
Key Achievements
Top Papers
- 1