Ph.D. Student, School of Automation Science and Engineering, South China University of Technology, Chinaπ
Dengxiang Liang is a dedicated Ph.D. student at the School of Automation Science and Engineering, South China University of Technology. His research focuses on control science and engineering, specializing in dynamic learning control, multi-agent cooperative control, and electric vehicle virtual energy storage. With a strong academic foundation, he has actively contributed to national research projects and has published multiple high-impact SCI papers. His achievements have been recognized with prestigious awards, including the National Scholarship and the Outstanding Graduate Student Award. Additionally, his industry experience, gained through an internship at the China Electric Power Research Institute, has strengthened his expertise in real-world energy storage applications.
Profile
Scopus
Education π
Dengxiang Liang holds a bachelorβs degree in Measurement and Control Technology & Instruments and a masterβs degree in Control Engineering from North China Electric Power University. Currently, he is pursuing a Ph.D. in Control Science and Engineering at South China University of Technology. His academic journey has equipped him with in-depth knowledge of automation, dynamic system modeling, and optimization control strategies, which he applies to his cutting-edge research in learning-based control methods for nonlinear systems.
Experience π’
Dengxiang Liang has extensive research and professional experience in control engineering. As a Ph.D. researcher, he has been actively involved in the study of deterministic learning control for discrete nonlinear systems, focusing on improving transient control performance and robustness in sampled-data environments. His expertise extends to industry applications, demonstrated through his internship at the China Electric Power Research Institute, where he researched the role of electric vehicles as energy storage units within smart grids. His contributions in both academia and industry have played a vital role in advancing energy-efficient solutions and intelligent control mechanisms.
Research Interests π¬
Dengxiang Liang’s research interests lie in dynamic learning control of sampled nonlinear systems, multi-agent cooperative learning control, and robust tube-model predictive control (MPC). He is also deeply engaged in visual control of mechanical arms, dynamic pattern recognition, and parameter identification. His work extends to multi-objective optimization strategies for electric vehicle virtual energy storage and the development of battery capacity decay models. His interdisciplinary approach aims to bridge theoretical advancements with practical applications in automation and intelligent control.
Awards π
Dengxiang Liang has been honored with multiple awards for his outstanding academic and research contributions. He received the prestigious National Scholarship, recognizing his excellence in control engineering research. Additionally, he was named an Outstanding Graduate Student for his exceptional academic performance and impactful research publications. His contributions to the field of automation and control science have earned him recognition at both institutional and national levels.
Publications π
An optimization scheduling method of electric vehicle virtual energy storage
International Journal of Energy Research, 2020, Cited by 20+ articles.
Link
Masterβslave game optimization method of smart energy systems
International Journal of Energy Research, 2021, Cited by 15+ articles.
Link
Adaptability assessment method of energy storage working conditions
Journal of Energy Storage, 2020, Cited by 25+ articles.
Link
Neural learning control for sampledβdata nonlinear systems
International Journal of Robust and Nonlinear Control, 2023.
Link
Disturbance Observer-Based Dynamic Learning Control
China Automation Congress (CAC), IEEE, 2023.
Link
Rapid dynamical learning from neural control of sampled-data nonlinear systems
Journal of the Franklin Institute.
Link
Rapid dynamical pattern modeling for sampled-data nonlinear systems
Nonlinear Dynamics.
Link
Conclusion
Dengxiang Liang is a highly deserving candidate for the Best Research Scholar Award due to his strong academic record, impactful research, and significant contributions to automation and energy storage. With continued focus on industry collaboration and innovation, his work will further shape the field of control engineering and sustainable energy solutions.