Fabio Bonassi

407 total citations
19 papers, 239 citations indexed

About

Fabio Bonassi is a scholar working on Control and Systems Engineering, Artificial Intelligence and Electrical and Electronic Engineering. According to data from OpenAlex, Fabio Bonassi has authored 19 papers receiving a total of 239 indexed citations (citations by other indexed papers that have themselves been cited), including 19 papers in Control and Systems Engineering, 7 papers in Artificial Intelligence and 5 papers in Electrical and Electronic Engineering. Recurrent topics in Fabio Bonassi's work include Fault Detection and Control Systems (12 papers), Advanced Control Systems Optimization (11 papers) and Neural Networks and Applications (7 papers). Fabio Bonassi is often cited by papers focused on Fault Detection and Control Systems (12 papers), Advanced Control Systems Optimization (11 papers) and Neural Networks and Applications (7 papers). Fabio Bonassi collaborates with scholars based in Italy, Sweden and Switzerland. Fabio Bonassi's co-authors include Riccardo Scattolini, Marcello Farina, Alessio La Bella, Lorenzo Fagiano, Giulio Panzani, Donato Zarrilli, Thomas B. Schön, Per Mattsson and Jing Xie and has published in prestigious journals such as Automatica, Systems & Control Letters and Electric Power Systems Research.

In The Last Decade

Fabio Bonassi

17 papers receiving 220 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Fabio Bonassi Italy 7 183 77 54 27 12 19 239
Tirumalasetty Chiranjeevi India 10 277 1.5× 47 0.6× 217 4.0× 5 0.2× 15 1.3× 39 374
Belal Abozalam Egypt 9 207 1.1× 44 0.6× 125 2.3× 8 0.3× 14 1.2× 27 286
Jinsong He Singapore 9 251 1.4× 17 0.2× 242 4.5× 11 0.4× 17 1.4× 26 324
C.J. Lopez-Toribio United Kingdom 7 262 1.4× 86 1.1× 27 0.5× 6 0.2× 16 1.3× 13 296
A.M. Pertew Canada 7 367 2.0× 46 0.6× 17 0.3× 18 0.7× 32 2.7× 13 394
Chokri Mechmeche Tunisia 11 447 2.4× 46 0.6× 28 0.5× 11 0.4× 31 2.6× 26 464
Joseph J. Yamé France 10 232 1.3× 22 0.3× 48 0.9× 8 0.3× 35 2.9× 51 275
Ghareeb Moustafa Egypt 12 105 0.6× 131 1.7× 204 3.8× 5 0.2× 13 1.1× 31 344
Juan Carlos Travieso‐Torres Chile 11 231 1.3× 13 0.2× 155 2.9× 13 0.5× 27 2.3× 42 301
Torsten Koller Germany 4 136 0.7× 85 1.1× 15 0.3× 4 0.1× 10 0.8× 6 205

Countries citing papers authored by Fabio Bonassi

Since Specialization
Citations

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

Fields of papers citing papers by Fabio Bonassi

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Fabio Bonassi

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

All Works

19 of 19 papers shown
1.
Xie, Jing, Fabio Bonassi, & Riccardo Scattolini. (2024). Learning Control Affine Neural NARX Models for Internal Model Control Design. IEEE Transactions on Automation Science and Engineering. 22. 8137–8149.
2.
Bonassi, Fabio, et al.. (2024). Structured state-space models are deep Wiener models. IFAC-PapersOnLine. 58(15). 247–252. 1 indexed citations
3.
Bonassi, Fabio, et al.. (2024). Estimation and MPC control based on gated recurrent unit neural networks with unknown disturbances. Virtual Community of Pathological Anatomy (University of Castilla La Mancha). 120–125.
4.
Bonassi, Fabio, et al.. (2023). Robust offset‐free nonlinear model predictive control for systems learned by neural nonlinear autoregressive exogenous models. International Journal of Robust and Nonlinear Control. 33(16). 9992–10009. 4 indexed citations
5.
Bonassi, Fabio, Alessio La Bella, Marcello Farina, & Riccardo Scattolini. (2023). Nonlinear MPC design for incrementally ISS systems with application to GRU networks. Automatica. 159. 111381–111381. 12 indexed citations
6.
Bonassi, Fabio, Alessio La Bella, Giulio Panzani, Marcello Farina, & Riccardo Scattolini. (2023). Deep Long-Short Term Memory networks: Stability properties and Experimental validation. Virtual Community of Pathological Anatomy (University of Castilla La Mancha). 1–6. 3 indexed citations
7.
Bonassi, Fabio, et al.. (2022). On Recurrent Neural Networks for learning-based control: Recent results and ideas for future developments. Journal of Process Control. 114. 92–104. 54 indexed citations
8.
Bonassi, Fabio & Riccardo Scattolini. (2022). Recurrent Neural Network-based Internal Model Control design for stable nonlinear systems. European Journal of Control. 65. 100632–100632. 18 indexed citations
9.
Bonassi, Fabio, et al.. (2022). An Offset-Free Nonlinear MPC scheme for systems learned by Neural NARX models. 2022 IEEE 61st Conference on Decision and Control (CDC). 2123–2128. 4 indexed citations
10.
Bonassi, Fabio, et al.. (2022). Towards lifelong learning of Recurrent Neural Networks for control design. 2022 European Control Conference (ECC). 2018–2023. 4 indexed citations
11.
Bonassi, Fabio, Marcello Farina, & Riccardo Scattolini. (2021). On the stability properties of Gated Recurrent Units neural networks. Systems & Control Letters. 157. 105049–105049. 32 indexed citations
12.
Bonassi, Fabio, Marcello Farina, & Riccardo Scattolini. (2021). Stability of discrete-time feed-forward neural networks in NARX configuration. IFAC-PapersOnLine. 54(7). 547–552. 24 indexed citations
13.
Bonassi, Fabio, et al.. (2021). Learning model predictive control with long short‐term memory networks. International Journal of Robust and Nonlinear Control. 31(18). 8877–8896. 49 indexed citations
14.
Bonassi, Fabio, et al.. (2021). Supervised control of hybrid AC-DC grids for power balance restoration. Electric Power Systems Research. 196. 107107–107107. 6 indexed citations
15.
Bella, Alessio La, et al.. (2020). A hierarchical approach for balancing service provision by microgrids aggregators. IFAC-PapersOnLine. 53(2). 12930–12935. 4 indexed citations
16.
Bella, Alessio La, et al.. (2020). A fully distributed control scheme for power balancing in distribution networks. IFAC-PapersOnLine. 53(2). 13178–13183. 4 indexed citations
17.
Bonassi, Fabio, et al.. (2020). Software-in-the-loop testing of a distributed optimal scheduling strategy for microgrids’ aggregators. Virtual Community of Pathological Anatomy (University of Castilla La Mancha). 985–989. 5 indexed citations
18.
Bonassi, Fabio, et al.. (2020). LSTM Neural Networks: Input to State Stability and Probabilistic Safety Verification. Virtual Community of Pathological Anatomy (University of Castilla La Mancha). 120. 85–94. 5 indexed citations
19.
Bella, Alessio La, Fabio Bonassi, Marcello Farina, & Riccardo Scattolini. (2019). Two-layer model predictive control of systems with independent dynamics and shared control resources. IFAC-PapersOnLine. 52(3). 96–101. 10 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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