Daniel E. Quevedo

12.5k total citations · 5 hit papers
267 papers, 9.4k citations indexed

About

Daniel E. Quevedo is a scholar working on Control and Systems Engineering, Computer Networks and Communications and Electrical and Electronic Engineering. According to data from OpenAlex, Daniel E. Quevedo has authored 267 papers receiving a total of 9.4k indexed citations (citations by other indexed papers that have themselves been cited), including 191 papers in Control and Systems Engineering, 107 papers in Computer Networks and Communications and 82 papers in Electrical and Electronic Engineering. Recurrent topics in Daniel E. Quevedo's work include Stability and Control of Uncertain Systems (110 papers), Advanced Control Systems Optimization (82 papers) and Control Systems and Identification (50 papers). Daniel E. Quevedo is often cited by papers focused on Stability and Control of Uncertain Systems (110 papers), Advanced Control Systems Optimization (82 papers) and Control Systems and Identification (50 papers). Daniel E. Quevedo collaborates with scholars based in Australia, Germany and Sweden. Daniel E. Quevedo's co-authors include Ricardo P. Aguilera, Patricio Cortés, Graham C. Goodwin, José Rodríguez, Tobias Geyer, Ling Shi, Marian P. Kaźmierkowski, Ralph Kennel, Subhrakanti Dey and Yuzhe Li and has published in prestigious journals such as IEEE Transactions on Automatic Control, IEEE Transactions on Industrial Electronics and Automatica.

In The Last Decade

Daniel E. Quevedo

257 papers receiving 9.2k citations

Hit Papers

Predictive Control in Power Electronics and Drives 2008 2026 2014 2020 2008 2015 2014 2014 2016 400 800 1.2k

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Daniel E. Quevedo Australia 46 6.6k 4.9k 2.8k 761 371 267 9.4k
Chen Peng China 54 8.9k 1.4× 1.8k 0.4× 6.9k 2.5× 987 1.3× 974 2.6× 299 11.1k
Vijay Gupta United States 32 2.9k 0.4× 1.3k 0.3× 2.1k 0.8× 778 1.0× 286 0.8× 227 4.6k
Fuwen Yang China 50 6.7k 1.0× 1.6k 0.3× 4.1k 1.5× 1.5k 1.9× 536 1.4× 226 8.7k
Soummya Kar United States 42 2.4k 0.4× 2.9k 0.6× 4.1k 1.5× 1.9k 2.5× 267 0.7× 230 7.2k
Ali Davoudi United States 51 8.8k 1.3× 9.2k 1.9× 2.3k 0.8× 490 0.6× 464 1.3× 227 12.0k
Henrik Sandberg Sweden 39 6.3k 1.0× 2.8k 0.6× 3.8k 1.3× 1.8k 2.3× 319 0.9× 258 8.5k
Xiaohua Ge Australia 55 9.4k 1.4× 2.7k 0.6× 8.8k 3.2× 1.8k 2.4× 846 2.3× 169 13.5k
Jiahu Qin China 48 3.5k 0.5× 1.5k 0.3× 5.7k 2.0× 688 0.9× 589 1.6× 205 7.8k
Derui Ding China 56 8.2k 1.2× 1.9k 0.4× 7.3k 2.6× 2.6k 3.4× 674 1.8× 237 11.8k
Ge Guo China 50 5.7k 0.9× 1.6k 0.3× 3.6k 1.3× 683 0.9× 608 1.6× 417 8.4k

Countries citing papers authored by Daniel E. Quevedo

Since Specialization
Citations

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

Fields of papers citing papers by Daniel E. Quevedo

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Daniel E. Quevedo. 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 Daniel E. Quevedo. The network helps show where Daniel E. Quevedo may publish in the future.

Co-authorship network of co-authors of Daniel E. Quevedo

This figure shows the co-authorship network connecting the top 25 collaborators of Daniel E. Quevedo. A scholar is included among the top collaborators of Daniel E. Quevedo 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 Daniel E. Quevedo. Daniel E. Quevedo 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.
Quevedo, Daniel E., et al.. (2025). Data Security and Privacy for AI-Enabled Smart Manufacturing. Engineering. 52. 34–39.
3.
Darup, Moritz Schulze, et al.. (2024). Extending Direct Data-Driven Predictive Control Towards Systems with Finite Control Sets. 3345–3350. 1 indexed citations
4.
Quevedo, Daniel E., et al.. (2024). Privacy-Preserving State Estimation in the Presence of Eavesdroppers: A Survey. IEEE Transactions on Automation Science and Engineering. 22. 6190–6207. 4 indexed citations
5.
Varma, Vineeth S., Romain Postoyan, Daniel E. Quevedo, & Irinel‐Constantin Morărescu. (2023). Event-Triggered Transmission Policies for Nonlinear Control Systems Over Erasure Channels. IEEE Control Systems Letters. 7. 2113–2118. 2 indexed citations
6.
Varma, Vineeth S., Romain Postoyan, Daniel E. Quevedo, & Irinel‐Constantin Morărescu. (2022). Transmission Power Policies for Energy-Efficient Wireless Control of Nonlinear Systems. IEEE Transactions on Automatic Control. 68(6). 3362–3376. 5 indexed citations
7.
Guo, Ziyang, Dawei Shi, Daniel E. Quevedo, & Ling Shi. (2018). Secure State Estimation Against Integrity Attacks: A Gaussian Mixture Model Approach. IEEE Transactions on Signal Processing. 67(1). 194–207. 58 indexed citations
8.
Darup, Moritz Schulze, et al.. (2018). Encrypted Cooperative Control Based on Structured Feedback. IEEE Control Systems Letters. 3(1). 37–42. 58 indexed citations
9.
Chatterjee, Debasish, et al.. (2017). Resource efficient stochastic predictive control under packet dropouts. IET Control Theory and Applications. 11(11). 1666–1673. 4 indexed citations
10.
Darup, Moritz Schulze, et al.. (2017). Towards Encrypted MPC for Linear Constrained Systems. IEEE Control Systems Letters. 2(2). 195–200. 97 indexed citations
11.
Chatterjee, Debasish, et al.. (2017). Stabilizing Stochastic Predictive Control Under Bernoulli Dropouts. IEEE Transactions on Automatic Control. 63(6). 1579–1590. 19 indexed citations
12.
Chatterjee, Debasish, et al.. (2016). Characterization of maximum hands-off control. Systems & Control Letters. 94. 31–36. 33 indexed citations
13.
Jurado, Isabel, Manuel G. Ortega, Daniel E. Quevedo, & Francisco R. Rubio. (2013). An H suboptimal robust control approach for systems with uncertainties and data dropouts. International Journal of Systems Science. 46(11). 1971–1981. 2 indexed citations
14.
Quevedo, Daniel E. & Vijay Gupta. (2011). Stability of sequence-based anytime control with Markovian processor availability. QUT ePrints (Queensland University of Technology). 56–61. 1 indexed citations
15.
Goodwin, Graham C., et al.. (2010). Opportunities and challenges in the application of advanced control to power electronics and drives. 27–39. 14 indexed citations
16.
Quevedo, Daniel E., Anders Åhlén, & Graham C. Goodwin. (2009). Predictive power control of wireless sensor networks for closed loop control. Lecture notes in control and information sciences. 215–224. 3 indexed citations
17.
Silva, Eduardo I., Milan S. Derpich, Jan Østergaard, & Daniel E. Quevedo. (2008). Proceedings of the IEEE Conference on Decision and Control. NOVA (University of Newcastle, Australia). 56 indexed citations
18.
Quevedo, Daniel E. & Graham C. Goodwin. (2004). When is the naive quantized control law globally optimal. QUT ePrints (Queensland University of Technology). 3. 1468–1476. 2 indexed citations
19.
Goodwin, Graham C., et al.. (2003). Moving horizon optimal quantizer for audio signals. Journal of the Audio Engineering Society. 51(3). 138–149. 18 indexed citations
20.
Goodwin, Graham C. & Daniel E. Quevedo. (2003). Finite Alphabet Control and Estimation. International Journal of Control Automation and Systems. 1(4). 412–430. 21 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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