Dr. Pengtao Song | Control Science | Best Researcher Award

Student, Xi’an Jiaotong University, China

Pengtao Song is a dedicated Ph.D. student at Xi’an Jiaotong University, China, specializing in Control Science and Engineering. With a strong foundation in automation from Northeastern University, he has been actively involved in groundbreaking research in cyber-physical systems, networked control, and intelligent control of industrial processes. His expertise extends to the diagnosis and control of mechatronic systems, with a focus on improving the robustness and efficiency of complex engineering networks. As a member of the Chinese Association of Automation (CAA), Pengtao continues to contribute significantly to the field of control engineering.

Publication Profile

• Scopus
• ORCID
• Google scholar

🎓 Education

Pengtao Song earned his B.S. degree in Automation from Northeastern University, Qinhuangdao, China, in 2020. Currently, he is pursuing his Ph.D. in the Faculty of Electronics and Information Engineering at Xi’an Jiaotong University, where he focuses on cyber-physical systems and networked control.

💼 Experience

Pengtao has been actively engaged in advanced research projects, including the prestigious National Science and Technology Major Project of China. His collaboration with the Logistics Science and Innovation Integration Development Research Center at Xi’an Jiaotong University has further enriched his experience in applied research and industrial innovation. His work has been recognized in high-impact journals such as Aerospace Science and Technology, IEEE Transactions on Automation Science and Engineering, and Nonlinear Dynamics, demonstrating his expertise in system modeling and intelligent control.

🏆 Awards and Honors

Pengtao Song’s outstanding contributions to control engineering have earned him recognition in the academic community. His publications in SCI and Scopus-indexed journals have made a significant impact, contributing to the advancement of cyber-physical systems. His research excellence has positioned him as a strong candidate for the Best Researcher Award in the field of Computer Science.

🔍 Research Focus

Pengtao’s research is centered on ensuring the efficient scheduling and robust operation of aero-engine networked systems. His work addresses challenges such as model perturbation, external disturbances, and communication delays, offering innovative control strategies like sliding mode control (SMC), H∞ control, fuzzy control, and data-driven control. His findings enhance the response performance of complex engineering systems, making significant contributions to the field of control and automation.

🚀Conclusion

Pengtao Song is an emerging leader in the field of control science and automation, contributing cutting-edge solutions to complex engineering challenges. His research in robust control strategies for networked systems has been widely recognized in top-tier journals, making a significant impact in the domain. As he continues his Ph.D. journey, his work promises to drive advancements in intelligent control, cyber-physical systems, and industrial automation.

📚 Publications

Pengtao Song has published extensively in leading journals and conferences, contributing valuable insights into control science and automation. Some of his notable works include:

Disturbance-Compensation-Based Predictive Sliding Mode Control for Aero-Engine Networked Systems With Multiple Uncertainties – IEEE Transactions on Automation Science and Engineering (2025)
DOI: 10.1109/TASE.2024.3350020

Charge or Pick Up? Optimizing E-Taxi Management: A Dual-Stage Heuristic Coordinated Reinforcement Learning Approach – IEEE Transactions on Automation Science and Engineering (2024)
DOI: 10.1109/TASE.2024.3486342

Model-free event-triggered resilient control for discrete-time nonlinear systems under sparse actuator attacks via GrHDP – Nonlinear Dynamics (2024)
DOI: 10.1007/s11071-024-10477-2

Observer-based H∞ Sliding Mode Control for Networked Systems with Stochastic Communication Protocol and Packet Loss– 14th Asian Control Conference (ASCC 2024)
EID: 2-s2.0-85205697035

TAMTL: A Novel Meta-Transfer Learning Approach for Fault Diagnosis of Rotating Machinery – 14th Asian Control Conference (ASCC 2024)
EID: 2-s2.0-85205678502

Fuzzy H∞ robust control for T-S aero-engine systems with network-induced factors under round-robin-like protocol – Aerospace Science and Technology (2023)
DOI: 10.1016/j.ast.2023.108258

Observer-based output feedback control for networked systems with dual-channel event-triggered sampling and quantization – Journal of the Franklin Institute (2022)
DOI: 10.1016/j.jfranklin.2022.07.042