Dr. Dingguo Liang | Error Detection Award | Best Researcher Award

🎓Education

Dingguo Liang is pursuing a Ph.D. in Dynamical Systems and Control at Peking University (2019–2024), with coursework encompassing linear system theory, robust and optimal control, and advanced linear algebra for system applications. He previously earned dual bachelor’s degrees in Theoretical and Applied Mechanics (B.S.) and Economics (B.Ec.) from Peking University (2015–2019), demonstrating a strong interdisciplinary foundation. Throughout his academic journey, Dingguo has consistently excelled in rigorous theoretical studies while integrating practical applications to address complex engineering challenges.

🏢Experience 

Dingguo Liang has substantial research experience in fault-tolerant control and estimation, focusing on distributed systems. He has developed innovative methods, including robust state estimation for linear and nonlinear systems, adaptive fuzzy-based fault detection, and data-driven predictive control leveraging Willems’ fundamental lemma. His solutions, often featuring plug-and-play scalability, are designed for real-world applications. Beyond research, Dingguo served as a teaching assistant, mentoring over 100 undergraduates in higher algebra, guiding their exercises, homework, and exams. Additionally, he contributes as a reviewer for prestigious journals, advancing knowledge dissemination in his field.

🏅Awards and Honors

Dingguo Liang’s achievements have been recognized through numerous honors, including the Dean’s Graduate Scholarship (2022–2023), Merit Student Award (2021, 2022), Suzhou Industrial Park Scholarship (2022), and Wong Lo Kat Scholarship (2021). These accolades highlight his academic dedication, research excellence, and commitment to innovation in system control and estimation.

🔬Research Focus

Dingguo Liang’s research centers on distributed control and estimation, fault diagnosis and tolerant control, cyber-physical system security, and data-driven estimation techniques. His work addresses critical challenges in anomaly detection, robust estimation, and optimization for interconnected and large-scale systems. Utilizing advanced methods such as adaptive fuzzy approximators and predictive control frameworks, Dingguo’s research contributes significantly to enhancing system reliability and security in real-world applications.

Publication Top Notes:

• Distributed Fault Estimation and Fault-Tolerant Control of Interconnected Systems With Plug-and-Play Features

  • Citations: 3

• Data-Driven Distributed Collaborative Fault Detection and Isolation for Large-Scale Dynamic Processes in Simultaneous-Fault Cases

  • Citations: 3

• Fault Diagnosis Scheme for the Rotary Machine Group: A Deep Mutual Learning-Based Approach With Cloud-Edge-End Collaboration

  • Citations: 4

• Distributed Fault Detection for Uncertain Lipschitz Nonlinear Multi-Agent Systems in Finite Frequency Domain

  • Citations: 5

• Plug-and-Play Robust Distributed Fault Estimation for Interconnected Systems

  • Citations: 7