Hai‐Peng Ren

Xi'an University of Technology, National Oceanic and Atmospheric Administration

Papers

Total Citations

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About

Dr. Hai-Peng Ren is a leading researcher in intelligent control systems and multi-agent coordination, with a focus on bridging classical control theory and modern reinforcement learning. His most-cited work, "Adaptive control of hydraulic position servo system using output feedback" (2017, 29 citations), addresses the critical challenge of high-precision tracking in hydraulic systems—widely used in industrial automation for their high power-to-size ratio and fast response. By developing an output-feedback adaptive control strategy, Dr. Ren has contributed to more reliable and accurate control of these essential systems, directly impacting manufacturing and robotics applications. More recently, his 2025 paper on "A Coordination Optimization Framework for Multi-Agent Reinforcement Learning" introduces innovative methods for reward redistribution and experience reutilization, tackling persistent challenges in cooperative MARL such as learning efficiency and scalability. This work holds promise for autonomous robot swarms and decentralized decision-making. With a career spanning foundational control theory to cutting-edge AI-driven coordination, Dr. Ren’s research demonstrates a consistent commitment to solving real-world automation problems, making his contributions valuable to both industrial practitioners and academic researchers in control and intelligent systems.