Ming‐Feng Ge

4.3k total citations · 1 hit paper
172 papers, 3.2k citations indexed

About

Ming‐Feng Ge is a scholar working on Computer Networks and Communications, Control and Systems Engineering and Electrical and Electronic Engineering. According to data from OpenAlex, Ming‐Feng Ge has authored 172 papers receiving a total of 3.2k indexed citations (citations by other indexed papers that have themselves been cited), including 107 papers in Computer Networks and Communications, 103 papers in Control and Systems Engineering and 32 papers in Electrical and Electronic Engineering. Recurrent topics in Ming‐Feng Ge's work include Distributed Control Multi-Agent Systems (93 papers), Adaptive Control of Nonlinear Systems (65 papers) and Neural Networks Stability and Synchronization (53 papers). Ming‐Feng Ge is often cited by papers focused on Distributed Control Multi-Agent Systems (93 papers), Adaptive Control of Nonlinear Systems (65 papers) and Neural Networks Stability and Synchronization (53 papers). Ming‐Feng Ge collaborates with scholars based in China, South Korea and United States. Ming‐Feng Ge's co-authors include Leimin Wang, Zhi‐Wei Liu, Jie Liu, Chang‐Duo Liang, Zhigang Zeng, Xingxing Jiang, Teng‐Fei Ding, Yiben Liu, Guang Ling and Xiang‐Yu Yao and has published in prestigious journals such as Automatica, IEEE Transactions on Industrial Electronics and Optics Express.

In The Last Decade

Ming‐Feng Ge

154 papers receiving 3.2k citations

Hit Papers

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What are hit papers?

Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the same subfield and year (this is the minimum needed to enter the top 1%, not the average within it), or reaches the top citation threshold in at least one of its specific research topics.

A review on state of health estimations and remaining useful life prognostics of lithium-ion batteries

2021 304 citations Ming‐Feng Ge, Jie Liu et al. profile →

Author Peers

Peers are selected by citation overlap in the author's most active subfields. citations · hero ref

Author						Papers	Cites
Ming‐Feng Ge	1.7k	1.6k	817	398	346	172	3.2k
Muhammad Rehan	1.3k 0.8×	1.7k 1.0×	792 1.0×	339 0.9×	169 0.5×	215	3.2k
Yanjun Shen	957 0.5×	1.8k 1.1×	611 0.7×	275 0.7×	126 0.4×	134	3.1k
Michaël Defoort	1.6k 0.9×	3.2k 1.9×	577 0.7×	168 0.4×	460 1.3×	137	4.4k
Haisheng Yu	1.0k 0.6×	3.3k 2.0×	1.2k 1.5×	247 0.6×	416 1.2×	186	4.5k
Mohammad Ali Badamchizadeh	653 0.4×	1.3k 0.8×	531 0.6×	240 0.6×	90 0.3×	141	2.5k
Farshad Khorrami	706 0.4×	3.1k 1.9×	810 1.0×	278 0.7×	193 0.6×	297	4.1k
Antonio Lorı́a	1.5k 0.9×	4.4k 2.7×	745 0.9×	442 1.1×	156 0.5×	205	5.6k
Hieu Trinh	1.7k 1.0×	5.1k 3.1×	1.5k 1.8×	398 1.0×	206 0.6×	283	6.4k
Alexander G. Loukianov	727 0.4×	3.2k 2.0×	1.1k 1.3×	225 0.6×	159 0.5×	241	4.1k
Youyi Wang	1.2k 0.7×	3.5k 2.1×	2.0k 2.4×	267 0.7×	657 1.9×	200	5.3k

Countries citing papers authored by Ming‐Feng Ge

Since Specialization

Citations

This map shows the geographic impact of Ming‐Feng Ge's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Ming‐Feng Ge with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Ming‐Feng Ge more than expected).

Fields of papers citing papers by Ming‐Feng Ge

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Ming‐Feng Ge. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Ming‐Feng Ge. The network helps show where Ming‐Feng Ge may publish in the future.

Co-authorship network of co-authors of Ming‐Feng Ge

This figure shows the co-authorship network connecting the top 25 collaborators of Ming‐Feng Ge. A scholar is included among the top collaborators of Ming‐Feng Ge based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Ming‐Feng Ge. Ming‐Feng Ge is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

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20 of 20 papers shown

Park, Ju H., et al.. (2025). Distributed Nash equilibrium seeking for multi-coalition games with constrained NASVs: A hierarchical prescribed-performance algorithm. Neurocomputing. 650. 130821–130821.

Ge, Ming‐Feng, et al.. (2025). SDF-Based Reinforcement Learning for Adaptive Path Planning and Formation Control of Multiagent Systems. IEEE Internet of Things Journal. 12(12). 19944–19954. 2 indexed citations

Ling, Guang, et al.. (2024). Identification of important nodes in multi-layer hypergraphs based on fuzzy gravity model and node centrality distribution characteristics. Chaos Solitons & Fractals. 188. 115503–115503. 6 indexed citations

Ge, Ming‐Feng, et al.. (2024). Optimal Path Seeking for Consensus Tracking Control of Networked Marine Surface Vehicles: A Three-Layer Reinforcement Learning Approach. 612–617. 1 indexed citations

Ge, Ming‐Feng, et al.. (2024). Hierarchical Q-Learning Path Planning for Cooperative Tracking Control of Multi-Agent Systems With Lumped Uncertainties. IEEE Transactions on Automation Science and Engineering. 22. 3866–3876. 2 indexed citations

Ge, Ming‐Feng, et al.. (2024). Hierarchical Piecewise-Trajectory Planning Framework for Autonomous Ground Vehicles Considering Motion Limitation and Energy Consumption. IEEE Internet of Things Journal. 11(18). 30145–30160. 4 indexed citations

Ding, Teng‐Fei, et al.. (2024). Secure Formation Tracking of Networked MSVs Under Actuator Constraints and DoS Attacks: Data-Driven Observer-Based Control. IEEE Transactions on Intelligent Vehicles. 10(2). 1414–1426. 2 indexed citations

Ding, Teng‐Fei, et al.. (2024). Performance-guaranteed prescribed-time bipartite consensus of networked Lagrangian agents with bounded inputs and signed digraphs. Journal of the Franklin Institute. 361(17). 107217–107217.

Ling, Guang, et al.. (2023). Multi-source variational mode transfer learning for enhanced PM2.5 concentration forecasting at data-limited monitoring stations. Expert Systems with Applications. 238. 121714–121714. 8 indexed citations

10.

Lai, Qiang, et al.. (2023). Adaptive finite-time projective synchronization of complex networks with nonidentical nodes and quantized time-varying delayed coupling. Information Sciences. 655. 119891–119891. 9 indexed citations

11.

Zhao, Xiao‐Wen, et al.. (2023). Neural adaptive predefined-time formation tracking control of multiple Euler–Lagrange systems with switching topologies based on hierarchical mechanism. Chaos Solitons & Fractals. 178. 114375–114375. 6 indexed citations

12.

Ding, Teng‐Fei, Ming‐Feng Ge, Zhi‐Wei Liu, Ming Chi, & Choon Ki Ahn. (2023). Cluster Time-Varying Formation-Containment Tracking of Networked Robotic Systems Via Hierarchical Prescribed-Time ESO-Based Control. IEEE Transactions on Network Science and Engineering. 11(1). 566–577. 11 indexed citations

13.

Yao, Xiang‐Yu, et al.. (2023). Hierarchical Nash equilibrium seeking strategies of quadratic time‐varying games with Euler–Lagrange players. International Journal of Robust and Nonlinear Control. 34(1). 666–680. 2 indexed citations

14.

Ding, Teng‐Fei, Ming‐Feng Ge, Zhi‐Wei Liu, Leimin Wang, & Jie Liu. (2023). Reinforcement Learning Formation Tracking of Networked Autonomous Surface Vehicles With Bounded Inputs via Cloud-Supported Communication. IEEE Transactions on Intelligent Vehicles. 9(1). 469–480. 8 indexed citations

15.

Ding, Teng‐Fei, et al.. (2023). Practical Resource Allocation of Networked Euler-Lagrange Agents With Quantized-Data Interactions and Arbitrary Bounded Uncertainties. IEEE Transactions on Network Science and Engineering. 11(2). 1442–1453. 1 indexed citations

16.

Ding, Teng‐Fei, et al.. (2023). Quantized-data resource allocation for reinforcement learning cooperative control of networked Euler-Lagrange agents with input saturation. Journal of the Franklin Institute. 360(16). 12076–12100. 1 indexed citations

17.

Gan, Jinqiang, et al.. (2021). Practical Bipartite Tracking for Networked Robotic Systems via Fixed‐Time Estimator‐Based Control. Complexity. 2021(1).

18.

Ge, Ming‐Feng, Chang‐Duo Liang, Xisheng Zhan, et al.. (2020). Multiple Time-Varying Formation of Networked Heterogeneous Robotic Systems via Estimator-Based Hierarchical Cooperative Algorithms. Complexity. 2020. 1–18. 4 indexed citations

19.

Liu, Zhi‐Wei, et al.. (2020). Trusted-Region Subsequence Reduction for Designing Resilient Consensus Algorithms. IEEE Transactions on Network Science and Engineering. 8(1). 259–268. 19 indexed citations

20.

Ge, Ming‐Feng, Zhi‐Wei Liu, Guanghui Wen, Xinghuo Yu, & Tingwen Huang. (2019). Hierarchical Controller-Estimator for Coordination of Networked Euler–Lagrange Systems. IEEE Transactions on Cybernetics. 50(6). 2450–2461. 76 indexed citations

Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.

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