Ning Tan

2.4k total citations
150 papers, 1.7k citations indexed

About

Ning Tan is a scholar working on Control and Systems Engineering, Biomedical Engineering and Mechanical Engineering. According to data from OpenAlex, Ning Tan has authored 150 papers receiving a total of 1.7k indexed citations (citations by other indexed papers that have themselves been cited), including 69 papers in Control and Systems Engineering, 55 papers in Biomedical Engineering and 30 papers in Mechanical Engineering. Recurrent topics in Ning Tan's work include Robotic Mechanisms and Dynamics (35 papers), Soft Robotics and Applications (29 papers) and Robot Manipulation and Learning (25 papers). Ning Tan is often cited by papers focused on Robotic Mechanisms and Dynamics (35 papers), Soft Robotics and Applications (29 papers) and Robot Manipulation and Learning (25 papers). Ning Tan collaborates with scholars based in China, Singapore and Sweden. Ning Tan's co-authors include Peng Yu, Yunong Zhang, Mohan Rajesh Elara, Deyue Yan, Guyu Xiao, Hongliang Ren, Xiaoyi Gu, Fenglei Ni, Binghuang Cai and Yiwen Yang and has published in prestigious journals such as Advanced Materials, Applied Physics Letters and Chemistry of Materials.

In The Last Decade

Ning Tan

132 papers receiving 1.6k citations

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name h Career Trend Papers Cites
Ning Tan China 23 696 626 382 373 253 150 1.7k
Samia Nefti‐Meziani United Kingdom 22 542 0.8× 1.0k 1.7× 179 0.5× 338 0.9× 240 0.9× 80 1.9k
Bin He China 26 398 0.6× 824 1.3× 358 0.9× 452 1.2× 298 1.2× 213 2.5k
Jian Gao China 25 566 0.8× 779 1.2× 769 2.0× 929 2.5× 679 2.7× 190 3.0k
Huihuan Qian China 22 442 0.6× 308 0.5× 288 0.8× 337 0.9× 395 1.6× 138 1.5k
Ligang Yao China 25 903 1.3× 560 0.9× 294 0.8× 855 2.3× 128 0.5× 118 2.1k
Yanbiao Li China 23 360 0.5× 348 0.6× 183 0.5× 412 1.1× 101 0.4× 119 1.5k
Houde Liu China 16 442 0.6× 388 0.6× 260 0.7× 182 0.5× 161 0.6× 116 1.1k
Qigao Fan China 21 315 0.5× 382 0.6× 588 1.5× 197 0.5× 94 0.4× 94 1.4k
Benliang Zhu China 29 808 1.2× 530 0.8× 374 1.0× 398 1.1× 145 0.6× 106 2.2k

Countries citing papers authored by Ning Tan

Since Specialization
Citations

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

Fields of papers citing papers by Ning Tan

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Ning Tan

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

All Works

20 of 20 papers shown
1.
Wu, Tunhua, Xi Chen, Ning Tan, et al.. (2025). Dissolved oxygen prediction in the Dianchi River basin with explainable artificial intelligence based on physical prior knowledge. Environmental Modelling & Software. 188. 106412–106412. 3 indexed citations
2.
Yu, Peng, et al.. (2024). Discrete integral-type zeroing neurodynamics for robust inverse-free and model-free motion control of redundant manipulators. Computers & Electrical Engineering. 118. 109344–109344. 2 indexed citations
3.
Tan, Ning, et al.. (2024). Design of C2S-CS low-calcium system for synergistic improvement of CO2 sequestration capacity and mechanical properties. Journal of Cleaner Production. 481. 144091–144091. 1 indexed citations
4.
Yu, Peng, et al.. (2024). Unifying Obstacle Avoidance and Tracking Control of Redundant Manipulators Subject to Joint Constraints: A New Data-Driven Scheme. IEEE Transactions on Cognitive and Developmental Systems. 16(5). 1861–1871. 2 indexed citations
5.
Zhang, Yunong, et al.. (2024). Adaptive ZNN Model and Solvers for Tackling Temporally Variant Quadratic Program With Applications. IEEE Transactions on Industrial Informatics. 20(11). 13015–13025. 9 indexed citations
6.
Chen, Lang, et al.. (2024). Grating magneto-optical trap optimization and drift-mitigation based on Bayesian learning. Applied Physics Letters. 124(6). 3 indexed citations
7.
Yu, Peng, Ning Tan, & Mingzhi Mao. (2023). Position-Based Visual Servo Control of Dual Robotic Arms With Unknown Kinematic Models: A Cerebellum- Inspired Approach. IEEE/ASME Transactions on Mechatronics. 28(4). 2328–2339. 15 indexed citations
8.
Tan, Ning & Peng Yu. (2023). Predefined-Time Convergent Kinematic Control of Robotic Manipulators With Unknown Models Based on Hybrid Neural Dynamics and Human Behaviors. IEEE Transactions on Neural Networks and Learning Systems. 35(12). 18026–18038. 6 indexed citations
9.
Tan, Ning, Peng Yu, & Fenglei Ni. (2022). New Varying-Parameter Recursive Neural Networks for Model-Free Kinematic Control of Redundant Manipulators With Limited Measurements. IEEE Transactions on Instrumentation and Measurement. 71. 1–14. 19 indexed citations
10.
11.
Tan, Ning, Peng Yu, & Fenglei Ni. (2022). A Cerebellum-Inspired Network Model and Learning Approaches for Solving Kinematic Tracking Control of Redundant Manipulators. IEEE Transactions on Cognitive and Developmental Systems. 15(1). 150–162. 17 indexed citations
12.
Yu, Peng, et al.. (2022). Comparative studies and performance analysis on neural-dynamics-driven control of redundant robot manipulators with unknown models. Engineering Applications of Artificial Intelligence. 117. 105528–105528. 9 indexed citations
13.
Tan, Ning, et al.. (2022). Two model-free schemes for solving kinematic tracking control of redundant manipulators using CMAC networks. Applied Soft Computing. 126. 109267–109267. 8 indexed citations
14.
Tan, Ning, et al.. (2022). A Discrete Model-Free Scheme for Fault-Tolerant Tracking Control of Redundant Manipulators. IEEE Transactions on Industrial Informatics. 18(12). 8595–8606. 28 indexed citations
15.
Tan, Ning, Peng Yu, Mao Zhang, & Changsheng Li. (2022). Toward Unified Adaptive Teleoperation Based on Damping ZNN for Robot Manipulators With Unknown Kinematics. IEEE Transactions on Industrial Electronics. 70(9). 9227–9236. 16 indexed citations
16.
Tan, Ning, et al.. (2022). Data-Driven Control for Continuum Robots Based on Discrete Zeroing Neural Networks. IEEE Transactions on Industrial Informatics. 19(5). 7088–7098. 34 indexed citations
17.
Zhang, Yunong, et al.. (2021). Concise Discrete ZNN Controllers for End-Effector Tracking and Obstacle Avoidance of Redundant Manipulators. IEEE Transactions on Industrial Informatics. 18(5). 3193–3202. 40 indexed citations
18.
Zhang, Yunong, et al.. (2021). Explicit Linear Left-and-Right 5-Step Formulas With Zeroing Neural Network for Time-Varying Applications. IEEE Transactions on Cybernetics. 53(2). 1133–1143. 14 indexed citations
19.
Tan, Ning, et al.. (2021). Neural-dynamics-enabled Jacobian inversion for model-based kinematic control of multi-section continuum manipulators. Applied Soft Computing. 103. 107114–107114. 12 indexed citations
20.
Pathmakumar, Thejus, et al.. (2017). Vision-Based Perception and Classification of Mosquitoes Using Support Vector Machine. Applied Sciences. 7(1). 51–51. 42 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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