Minghao Han

973 citations
42 papers · 629 · h-index 13

Impact in

Papers in

Minghao Han

36 papers receiving 610 citations

Peers

Minghao Han
Comparison fields: 5 of 76
  • Control and Systems Engineering 350
  • Automotive Engineering 107
  • Computational Mathematics 4
  • Computer Vision and Pattern Recognition 104
  • Artificial Intelligence 161
Replace Kim P. Wabersich with:
Kim P. Wabersich Switzerland
Andrea Carron Switzerland
Agustín Jiménez Spain
Melissa Greeff Canada
Sehraneh Ghaemi Iran
Ichiro Maruta Japan
Julian Berberich Germany
Masoud Abbaszadeh United States
Miguel Gabriel Villarreal-Cervantes Mexico
Lukas Brunke Canada
Minghao Han relative to Kim P. Wabersich Switzerland Kim P. Wabersich's profile →
Citations per field
00.5×6.5×
Kim P. Wabersich · 1×
Citations per year

Countries citing papers authored by Minghao Han

Since Specialization
Citations

This map shows the geographic impact of Minghao Han'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 Minghao Han with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Minghao Han more than expected).

Fields of papers citing papers by Minghao Han

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Minghao Han. 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 Minghao Han. The network helps show where Minghao Han may publish in the future.

Co-authors

The 25 scholars most cited alongside Minghao Han, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.

Border = papers with Minghao Han Line = papers co-authored together Minghao Han links everyone, so they are left out of the graph.

All Works

20 of 20 papers shown

Showing the 20 most-cited of 42 papers — load more, or switch the sort, to bring in the rest.

#Work
1 2021108
2 202086
3 201683
4 202149
5 201947
6 202246
7 202327
8 202223
9 202319
10 202419
11 202316
12 202215
13 202414
14 202011
15
H ∞ Model-free Reinforcement Learning with Robust Stability Guarantee.
201911
16 20237
17 20246
18 20245
19 20235
20 20215

About Minghao Han

Minghao Han is a scholar working on Control and Systems Engineering, Electrical and Electronic Engineering, Statistical and Nonlinear Physics, Artificial Intelligence and Automotive Engineering, having authored 42 papers that have together received 629 indexed citations. Recurring topics across this work include Advanced Control Systems Optimization (11 papers), Model Reduction and Neural Networks (9 papers), Reinforcement Learning in Robotics (6 papers), Multilevel Inverters and Converters (6 papers), Advanced DC-DC Converters (5 papers), Control Systems and Identification (4 papers), Advanced Battery Technologies Research (4 papers) and Stability and Control of Uncertain Systems (4 papers). The work is most often cited by research in Control and Systems Engineering (350 citations), Automotive Engineering (107 citations), Computational Mathematics (4 citations), Computer Vision and Pattern Recognition (104 citations) and Artificial Intelligence (161 citations). Minghao Han has collaborated with scholars based in China, Singapore and United States. Frequent co-authors include Lixian Zhang, Xunyuan Yin, Wei Pan, Zhaojian Li, Jun Wang, Ruixian Zhang, Ye Zhao, Rui Weng, Yuan Tian and Jianan Yang. Their work appears in journals such as Computers & Chemical Engineering, IEEE Transactions on Systems Man and Cybernetics Systems, IEEE Transactions on Neural Networks and Learning Systems, Information Sciences and Science China Technological Sciences.

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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