Haoyang Deng
Papers
1
Total Citations
10
H-Index
1
About
Haoyang Deng is a leading researcher in computational control and optimisation, with a primary focus on real-time nonlinear model predictive control (NMPC) for fast-sampling and large-scale applications. His most notable contribution is the development of ParNMPC, a highly parallelisable optimisation toolkit that addresses the critical challenge of computational efficiency in NMPC. By designing an implementation that leverages parallel computing architectures, Deng’s work enables real-time control in systems where traditional methods falter due to speed or scale constraints. The flagship paper on ParNMPC (2020) has garnered 10 citations, reflecting its growing influence in the control engineering community. This toolkit stands out for its practical impact, bridging the gap between theoretical NMPC algorithms and real-world deployment in robotics, autonomous systems, and industrial process control. Deng’s research is distinguished by its emphasis on algorithmic innovation combined with rigorous implementation, making advanced control strategies accessible for time-critical applications. His work continues to inspire new directions in parallel optimisation for embedded and high-performance control systems.
Research Focus
Key Achievements
Top Papers
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