Rong Long
Papers
1
Total Citations
8
H-Index
1
About
Rong Long is a researcher specializing in intelligent control systems, energy management, and hybrid power systems, with a particular focus on applying advanced machine learning techniques to real-world engineering challenges. His most recognized work, "Nonlinear Recurrent Neural Network Predictive Control for Energy Distribution of a Fuel Cell Powered Robot" (2014), exemplifies his expertise at the intersection of neural networks and power system optimization. In this influential study, Long developed a neural network predictive control strategy to optimize power distribution within a fuel cell and ultracapacitor hybrid power system for robotic applications. By leveraging time-variant auto-regressive moving average with exogenous (ARMAX) modeling combined with recurrent neural networks, his approach demonstrated a sophisticated solution to the inherently nonlinear challenges of hybrid energy management. This work has garnered 8 citations, reflecting its contribution to the growing field of intelligent energy control for autonomous systems. Long's research bridges theoretical machine learning frameworks with practical engineering applications, making his contributions particularly valuable for researchers working on sustainable robotics, fuel cell technology, and adaptive predictive control systems. His work continues to inform developments in energy-efficient autonomous platforms.
Research Focus
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Top Papers
- 1