Personal Information

Personal Information

        Ming-Zhe Dai is currently working at the School of Automation, Central South University, China. He received the Ph. D. degree in Control Science and Engineering from the School of Astronautics, Harbin Institute of Technology, China, in 2019. His current research interests are in the areas of group intelligence, digital control theory, distributed coordination control, spacecraft control, and machine learning. He has authored and co-authored more than 30 scientific papers published in peer-reviewed journals, including 1 ESI highly cited paper, receiving more than 150 citations in the past five years. In particular, his first-authored and corresponding-authored papers have appeared in several high-impact journals, including IEEE Trans. Syst., Man, Cybern., IEEE Trans. Cybern., IEEE Trans. Aerosp. Electron. Syst., and Int. J. Robust Nonlinear Control. He has been invited as peer reviewers for 10+ journals, including IEEE Trans. Autom. Control, IEEE Trans. Cybern., IEEE Trans. Syst., Man, Cybern., and IEEE Trans. Ind. Electron. He has presided over and participated in many scientific research projects, including the National Natural Science Foundation of China (NSFC) and China Aerospace Science and Technology Corporation (CASC) commissioned project. 

Research Overview

In the past five years, Ming-Zhe Dai’s academic research mainly focuses on distributed multi-agent coordinated control, sampled-data control, spacecraft control and prescribed performance control, which are summarized as follows:

    1. This scholar studied consensus and formation problems for multi-agent systems. The proposed prescribed performance control methods are suitable for multiple Euler-Lagrange systems and QUAD nonlinear multi-agent systems. These methods improve the state synchronization and tracking accuracies and improve robustness against external disturbances. The designed policies can be used for coordinated control of multi-UAVs and multi-manipulators and synchronization control of coupled chaotic circuits.

2. This scholar proposed distributed edge event- and self-triggered control policies for multi-agent networks. These intermittent Lebesgue sampling strategies schedule sensors, embedded computers, and communication devices on mobile robots to achieve information interaction with low resource consumption. These studies have the potential to be employed in the digital application of multiple-robot coordinated control.

3. This scholar designed event-triggered control policies for spacecraft attitude control. The investigated algorithms do not only save communication resources to deal with scenarios where internal communication between different modules is limited, but also can reduce the influences of external disturbances, inertia uncertainties, and actuator failure to improve state convergence accuracies. These theories can be employed in the digital control of plug-and-play modular spacecraft.

Teaching Experiences

l  2020 Spring—          Modern Control Theory for undergraduate students, 32 class hours

l  2022 Spring—         Machine Learning for master students, 32 class hours

Peer Reviewers

IEEE Transactions on Automatic Control

IEEE Transactions on Aerospace and Electronic Systems

IEEE Transactions on Cybernetics

IEEE Transactions on Systems, Man, and Cybernetics: Systems

IEEE Transactions on Control of Network Systems

IEEE Transactions on Industrial Informatics

IEEE Transactions on Industrial Electronics

IEEE Transactions on Circuits and Systems I: Regular Papers

IEEE Transactions on Circuits and Systems II: Express Briefs

IEEE Access

ISA Transactions

Nonlinear Dynamics

Circuits, Systems & Signal Processing

Aircraft Engineering and Aerospace Technology