Tom Oomen

4.4k total citations
312 papers, 3.2k citations indexed

About

Tom Oomen is a scholar working on Control and Systems Engineering, Mechanical Engineering and Civil and Structural Engineering. According to data from OpenAlex, Tom Oomen has authored 312 papers receiving a total of 3.2k indexed citations (citations by other indexed papers that have themselves been cited), including 277 papers in Control and Systems Engineering, 152 papers in Mechanical Engineering and 38 papers in Civil and Structural Engineering. Recurrent topics in Tom Oomen's work include Iterative Learning Control Systems (181 papers), Control Systems and Identification (114 papers) and Piezoelectric Actuators and Control (52 papers). Tom Oomen is often cited by papers focused on Iterative Learning Control Systems (181 papers), Control Systems and Identification (114 papers) and Piezoelectric Actuators and Control (52 papers). Tom Oomen collaborates with scholars based in Netherlands, United States and Japan. Tom Oomen's co-authors include Joost Bolder, Jurgen van Zundert, Frank Boeren, M. Steinbuch, О.H. Bosgra, Sjirk Koekebakker, Dennis Bruijnen, Marc van de Wal, Cristian R. Rojas and Marcel Heertjes and has published in prestigious journals such as SHILAP Revista de lepidopterología, IEEE Transactions on Automatic Control and IEEE Transactions on Industrial Electronics.

In The Last Decade

Tom Oomen

282 papers receiving 3.2k citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Tom Oomen Netherlands 30 2.8k 1.7k 424 309 215 312 3.2k
Arun Kumar Samantaray India 28 1.5k 0.6× 1.1k 0.6× 257 0.6× 271 0.9× 268 1.2× 112 2.5k
Svante Gunnarsson Sweden 25 2.7k 1.0× 1.1k 0.6× 399 0.9× 159 0.5× 129 0.6× 133 3.3k
Yu Guo China 28 1.7k 0.6× 1.3k 0.8× 141 0.3× 533 1.7× 223 1.0× 144 2.7k
Hee‐Jun Kang South Korea 32 3.2k 1.2× 1.6k 0.9× 300 0.7× 138 0.4× 300 1.4× 133 3.9k
Paolo Pennacchi Italy 35 2.5k 0.9× 2.8k 1.6× 171 0.4× 891 2.9× 304 1.4× 255 4.0k
Kefu Liu Canada 31 3.6k 1.3× 793 0.5× 370 0.9× 932 3.0× 226 1.1× 78 4.6k
Marcel Heertjes Netherlands 24 1.4k 0.5× 619 0.4× 247 0.6× 297 1.0× 129 0.6× 146 1.8k
Rong‐Fong Fung Taiwan 27 1.8k 0.7× 829 0.5× 212 0.5× 288 0.9× 420 2.0× 130 2.3k
Maxime Gautier France 29 2.6k 0.9× 1.2k 0.7× 725 1.7× 225 0.7× 57 0.3× 130 3.0k
Alberto Cardona Argentina 33 2.1k 0.8× 1.3k 0.8× 299 0.7× 996 3.2× 117 0.5× 122 3.8k

Countries citing papers authored by Tom Oomen

Since Specialization
Citations

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

Fields of papers citing papers by Tom Oomen

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Tom Oomen

This figure shows the co-authorship network connecting the top 25 collaborators of Tom Oomen. A scholar is included among the top collaborators of Tom Oomen 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 Tom Oomen. Tom Oomen 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.
Oomen, Tom, et al.. (2024). Randomized iterative feedback tuning for fast MIMO feedback design of a mechatronic system. Control Engineering Practice. 154. 106152–106152.
2.
Formentin, Simone, et al.. (2024). Efficient tuning for motion control in diverse systems: a Bayesian framework. IFAC-PapersOnLine. 58(15). 354–359.
3.
Heertjes, Marcel, et al.. (2024). Model-free control for an industrial long-stroke motion system with a nonlinear micropositioning actuator. Mechatronics. 104. 103257–103257. 3 indexed citations
4.
Pavlov, Alexey, et al.. (2024). Nonlinear iterative learning control for discriminating between disturbances. Automatica. 171. 111902–111902. 3 indexed citations
5.
Oomen, Tom, et al.. (2024). Random Learning Leads to Faster Convergence in ‘Model‐Free’ ILC: With Application to MIMO Feedforward in Industrial Printing. International Journal of Adaptive Control and Signal Processing. 39(7). 1521–1532.
6.
Dael, M. van, Gert Witvoet, B. L. Swinkels, et al.. (2024). Online decoupling of the time-varying longitudinal feedback loops for improved performance in Advanced Virgo Plus*. Classical and Quantum Gravity. 41(21). 215008–215008. 1 indexed citations
7.
Oomen, Tom, et al.. (2024). Scaled graphs for reset control system analysis. European Journal of Control. 80. 101050–101050. 2 indexed citations
8.
Oomen, Tom, et al.. (2024). Vibration Control Under Frequency-Varying Disturbances With Application to Satellites. IEEE Transactions on Control Systems Technology. 32(6). 1983–1994. 2 indexed citations
9.
Zhuang, Zhihe, Hongfeng Tao, Yiyang Chen, et al.. (2023). Optimal iterative learning control design for continuous-time systems with nonidentical trial lengths using alternating projections between multiple sets. Journal of the Franklin Institute. 360(5). 3825–3848. 3 indexed citations
10.
Hunnekens, Bram, et al.. (2023). An Estimation Perspective on Breathing Effort Disturbances in Mechanical Ventilation. IFAC-PapersOnLine. 56(2). 8215–8220. 2 indexed citations
11.
Pavlov, Alexey, et al.. (2023). Nonlinear Iterative Learning Control: A Frequency-Domain Approach for Fast Convergence and High Accuracy. IFAC-PapersOnLine. 56(2). 1889–1894. 4 indexed citations
12.
González, Rodrigo A., Koen Tiels, & Tom Oomen. (2023). Identifying Lebesgue-sampled Continuous-time Impulse Response Models: A Kernel-based Approach*. IFAC-PapersOnLine. 56(2). 4198–4203. 1 indexed citations
13.
Keulen, Thijs van, Tom Oomen, & W.P.M.H. Heemels. (2023). Online feedforward parameter learning with robustness to set-point variations. IFAC-PapersOnLine. 56(2). 1919–1925.
14.
Tiels, Koen, et al.. (2023). Memory-Element-Based Hysteresis: Identification and Compensation of a Piezoelectric Actuator. IEEE Transactions on Control Systems Technology. 31(6). 2863–2870. 6 indexed citations
15.
Hunnekens, Bram, et al.. (2023). Triggered Repetitive Control: Application to Mechanically Ventilated Patients. IEEE Transactions on Control Systems Technology. 31(4). 1581–1593. 2 indexed citations
16.
Tiels, Koen, et al.. (2023). A Wavelet-Based Approach to FRF Identification From Incomplete Data. IEEE Transactions on Instrumentation and Measurement. 72. 1–15.
17.
Hunnekens, Bram, et al.. (2022). Noninvasive Breathing Effort Estimation of Mechanically Ventilated Patients Using Sparse Optimization. SHILAP Revista de lepidopterología. 1. 57–68. 3 indexed citations
18.
Hunnekens, Bram, et al.. (2020). Adaptive Control for Mechanical Ventilation for Improved Pressure Support. IEEE Transactions on Control Systems Technology. 29(1). 180–193. 24 indexed citations
19.
Heertjes, Marcel, et al.. (2018). Experimental estimation of transmissibility matrices for industrial multi-axis vibration isolation systems. Mechanical Systems and Signal Processing. 107. 469–483. 17 indexed citations
20.
Oomen, Tom. (2018). Learning in machines : towards intelligent mechatronic systems through Iiterative control. TU/e Research Portal (Eindhoven University of Technology). 6. 5–11.

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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