Huagang Zhong

Papers

Total Citations

H-Index

About

Huagang Zhong is a researcher focused on intelligent control systems and autonomous vehicle navigation, with a particular emphasis on Automated Guided Vehicles (AGVs). His work centers on applying deep reinforcement learning to solve critical challenges in AGV motion control, specifically addressing smooth movement and PID parameter tuning. In his most cited paper, "The Method for Automatic Adjustment of AGV’s PID Based on Deep Reinforcement Learning" (2022, 5 citations), Zhong proposes a novel approach that leverages reinforcement learning to automatically optimize PID controller parameters, eliminating the need for manual tuning and improving AGV stability. His follow-up work, "The Determination of Reward Function in AGV Motion Control Based on DQN" (2022, 4 citations), further advances this line of research by exploring how reward functions can be designed to enhance Deep Q-Network-based motion control. Though early in his career, Zhong’s contributions are notable for bridging classical control theory with modern machine learning, offering practical solutions for industrial automation. His work is particularly relevant for researchers and engineers seeking to integrate AI into real-world robotic systems, and his growing citation count reflects the increasing interest in reinforcement learning for autonomous vehicle control.