Haishan Zhang
Papers
1
Total Citations
2
H-Index
1
About
Haishan Zhang is a rising researcher whose work lies at the intersection of optimization, control theory, and robotics, with a particular focus on real-time model predictive control (MPC) for complex, non-convex systems. His major contribution is the development of a real-time MPC framework using block successive convex approximation, which overcomes the long-standing challenge of implementing nonlinear MPC with extended prediction horizons on resource-constrained robotic platforms. By efficiently handling non-convex dynamics, his approach enables faster, more accurate contouring control—critical for applications in autonomous navigation and industrial robotics. Though early in his career, his 2024 paper has already garnered attention, accumulating 2 citations and signaling strong potential for future impact. Zhang’s work bridges the gap between theoretical optimization and practical deployment, offering a scalable solution for real-time decision-making in robotics. His research is particularly notable for its emphasis on computational efficiency without sacrificing control performance, making it highly relevant for students and engineers seeking to push the boundaries of autonomous systems.
Research Focus
Key Achievements
Top Papers
- 1