Mr. Bingyi Jia | Optimal Control | Best Researcher Award 

Bingyi Jia (born August 2000 in Heze, Shandong Province) is a PhD candidate in Mechanical Engineering at the School of Mechatronic Engineering, Shandong University of Science and Technology. He holds a direct-track MSc degree in Mechanical Engineering and completed a visiting research program at the Centre for Efficiency and Performance Engineering, University of Huddersfield (UK) in 2024. His research focuses on intelligent control, adaptive learning, and data-driven output-feedback systems, with applications in hydraulic supports, robotics, and mining equipment. Bingyi has published over ten peer-reviewed papers in top-tier journals such as IEEE Transactions on Industrial Electronics and Automatica, and has led or participated in multiple national and industrial research projects on intelligent mining technology and control systems.

Mr. Bingyi Jia | Shandong University of Science and Technology | China

Profile

SCOPUS

ORCID ID

🎓Education

  • Bingyi Jia began his academic journey in Mechanical Engineering at the Shandong University of Science and Technology, where he earned his Bachelor’s degree from 2019 to 2023. He continued at the same university in a direct PhD track, completing his Master’s degree in Mechanical Engineering from 2023 to 2025 under the supervision of Prof. Qingliang Zeng. Currently, he is pursuing a PhD in Mechanical Engineering at the School of Mechatronic Engineering, Shandong University of Science and Technology. In 2024, he broadened his international academic experience by serving as a visiting scholar at the Centre for Efficiency and Performance Engineering (CEPE), University of Huddersfield, UK.

👨‍🏫 Experience

  • Bingyi Jia has actively contributed to several high-impact research projects in mechanical engineering and intelligent control systems. As part of a National Natural Science Foundation of China key project, he helped develop adaptive optimal control algorithms using multi-sensor monitoring data for shield load and posture perception. In a major industry project focused on longwall mining technology for steeply inclined seams, he contributed to group-control algorithms that enhanced safety and operational stability. In addition, he has independently led multiple innovation projects at SDUST, including digital hydraulic components and cooperative control systems, demonstrating strong leadership and technical problem-solving abilities. He also headed a key innovation team project aimed at optimizing coal-cutter performance using online policy iteration.

🤝 Awards and Recognition

  • Bingyi Jia’s selection for prestigious national and industrial research projects, including funding from the National Natural Science Foundation and his appointment as team lead or principal investigator in several university-led innovation programs, indicate high recognition of his research capabilities and leadership potential. His international research engagement as a visiting scholar in the UK further affirms his academic excellence.

💡Skills and Certifications

  • Bingyi Jia specializes in intelligent control, adaptive learning, and data-driven output-feedback systems. His work demonstrates advanced skills in developing and implementing control algorithms, including adaptive dynamic programming, policy learning, and optimal control for complex mechanical systems such as hydraulic supports and robotic platforms. He is proficient in coordinating multi-agent systems, high-speed digital control architectures, and integrating sensor data for system optimization. His programming and algorithm development capabilities are complemented by strong practical engineering knowledge.

🔬 Research Focus

  • Bingyi Jia’s research focuses on intelligent perception and control in mechanical systems, particularly those involving hydraulic actuators and mining equipment. His work emphasizes data-driven and learning-based control methods for unmatched uncertainties, nonlinear systems, and real-time feedback scenarios. He is especially active in applying reinforcement learning techniques, such as Q-learning and adaptive dynamic programming, to challenges in trajectory tracking, pressure-flow regulation, and system synchronization. His interdisciplinary approach bridges control theory, mechanical systems, and computational intelligence, targeting real-world applications in smart manufacturing and mining automation.

🌎Conclusion

  • Bingyi Jia’s outstanding achievements in both theoretical research and practical engineering make him a compelling nominee for the Research for Best Researcher Award. His contributions reflect a deep commitment to advancing knowledge, addressing industry challenges, and pushing the boundaries of intelligent control systems. He is not only a top-performing researcher today but also a thought leader shaping the future of intelligent automation and adaptive control.

📖Publications

Control of Parallel Quadruped Robots Based on Adaptive Dynamic Programming Control
Authors: Liang Junwei, Shenyu Tang, Bingyi Jia*
Journal: Machines

An Optimal Control Method for Trajectory Tracking Error Detection of Autonomous Vehicles
Authors: Feng Pengfei, Bingyi Jia*, Jin Huiqing, Wang Guang
Journal: Applied Mathematics and Computer Science

Model‑Free H∞ Prescribed Performance Control of Adaptive Cruise Control Systems via Policy Learning
Authors: Jun Zhao, Bingyi Jia*, Ziliang Zhao
Journal: IEEE Transactions on Intelligent Transportation Systems

Robotic Systems Tracking Error Measurement: A Data‑Driven Output Feedback Control Method
Authors: Jun Zhao, Qiang Sun, Bingyi Jia*

Influence of Hydraulic Supply Pressure Loss on Advancing Speed of Hydraulic Supports
Authors: Guan Yantai, Niu Tianrui, Xu Bing, Liu Wei, Zheng Chen, Liu Yuanchao, Bingyi Jia*, Meng Zhaosheng
Journal: Machine Tool Hydraulics

Bingyi Jia | Optimal Control | Best Researcher Award