Manh Tuan

438 total citations
13 papers, 350 citations indexed

About

Manh Tuan is a scholar working on Control and Systems Engineering, Mechanical Engineering and Automotive Engineering. According to data from OpenAlex, Manh Tuan has authored 13 papers receiving a total of 350 indexed citations (citations by other indexed papers that have themselves been cited), including 6 papers in Control and Systems Engineering, 6 papers in Mechanical Engineering and 5 papers in Automotive Engineering. Recurrent topics in Manh Tuan's work include Hydraulic and Pneumatic Systems (6 papers), Vehicle Dynamics and Control Systems (5 papers) and Iterative Learning Control Systems (4 papers). Manh Tuan is often cited by papers focused on Hydraulic and Pneumatic Systems (6 papers), Vehicle Dynamics and Control Systems (5 papers) and Iterative Learning Control Systems (4 papers). Manh Tuan collaborates with scholars based in Australia, China and South Korea. Manh Tuan's co-authors include Hai Wang, Zhihong Man, Huifang Kong, Ming Yu, Jinchuan Zheng, Zhenwei Cao, Cishen Zhang, Yong Zhao, Linfeng Liu and Ping He and has published in prestigious journals such as IEEE Transactions on Industrial Electronics, IEEE Transactions on Vehicular Technology and Nonlinear Dynamics.

In The Last Decade

Manh Tuan

13 papers receiving 348 citations

Peers

Manh Tuan

CSE

AE

ME

EEE

CTM

Matthias Schultalbers Germany

Xin Deng China

Youhao Hu China

Damien Koenig France

Xiaohui Hou China

Wenguang Luo China

Darrel Recker United States

N. Giorgetti Italy

M. G. Skarpetis Greece

Jiangyan Zhang China

Matthias Schultalbers Germany

Manh Tuan

274 ×1.1

CSE

172 ×1.0

AE

90 ×0.6

ME

82 ×1.9

EEE

28 ×1.1

CTM

Citations per year, relative to Manh Tuan Manh Tuan (= 1×) peers Matthias Schultalbers

Countries citing papers authored by Manh Tuan

Since Specialization

Citations

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

Fields of papers citing papers by Manh Tuan

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Manh Tuan

This figure shows the co-authorship network connecting the top 25 collaborators of Manh Tuan. A scholar is included among the top collaborators of Manh Tuan 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 Manh Tuan. Manh Tuan is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

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13 of 13 papers shown

1.

Tuan, Manh & Kijung Shin. (2024). Unsupervised Alignment of Hypergraphs with Different Scales. 609–620. 2 indexed citations

2.

Tuan, Manh. (2024). Sliding mode learning control and its applications. Figshare. 1 indexed citations

3.

Tuan, Manh & Kijung Shin. (2023). Improving the core resilience of real-world hypergraphs. Data Mining and Knowledge Discovery. 37(6). 2438–2493. 1 indexed citations

4.

Tuan, Manh, Noseong Park, & Kijung Shin. (2022). Two-Stage Training of Graph Neural Networks for Graph Classification. Neural Processing Letters. 55(3). 2799–2823. 5 indexed citations

5.

Tuan, Manh, Jiong Jin, Zhihong Man, & Hai Wang. (2016). Discrete-time sliding mode learning based congestion control for connection-oriented communication networks. Swinburne Research Bank (Swinburne University of Technology). 9. 295–299. 1 indexed citations

6.

Wang, Hai, Linfeng Liu, Ping He, et al.. (2016). Robust adaptive position control of automotive electronic throttle valve using PID-type sliding mode technique. Nonlinear Dynamics. 85(2). 1331–1344. 41 indexed citations

7.

Wang, Hai, Zhihong Man, Huifang Kong, et al.. (2016). Design and Implementation of Adaptive Terminal Sliding-Mode Control on a Steer-by-Wire Equipped Road Vehicle. IEEE Transactions on Industrial Electronics. 63(9). 5774–5785. 148 indexed citations

8.

Tuan, Manh, Jiong Jin, Hai Wang, & Zhihong Man. (2016). Sliding mode learning based congestion control for DiffServ networks. IET Control Theory and Applications. 10(11). 1281–1287. 6 indexed citations

9.

Wang, Hai, Ping He, Ming Yu, et al.. (2016). Adaptive neural network sliding mode control for steer-by-wire-based vehicle stability control. Journal of Intelligent & Fuzzy Systems. 31(2). 885–902. 12 indexed citations

10.

Wang, Hai, Huifang Kong, Ming Yu, et al.. (2015). RBF-neural-network-based sliding mode controller of automotive Steer-by-Wire systems. 9. 907–914. 1 indexed citations

11.

Tuan, Manh, Zhihong Man, Jiong Jin, et al.. (2015). Sliding mode learning control of non‐minimum phase nonlinear systems. International Journal of Robust and Nonlinear Control. 26(11). 2281–2298. 9 indexed citations

12.

Wang, Hai, et al.. (2015). Neural-network-based robust control for steer-by-wire systems with uncertain dynamics. Neural Computing and Applications. 26(7). 1575–1586. 28 indexed citations

13.

Tuan, Manh, et al.. (2014). Robust Sliding Mode-Based Learning Control for Steer-by-Wire Systems in Modern Vehicles. IEEE Transactions on Vehicular Technology. 63(2). 580–590. 95 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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