M. Ariola

6.8k citations
150 papers · 4.2k indexed · 3 hit papers · h-index 27

M. Ariola

137 papers receiving 4.1k citations

Hit Papers

Finite-time stabilization via dynamic output feedback3752001202620092017250500750

Peers

M. Ariola
Comparison fields: 5 of 69
  • Control and Systems Engineering 3.0k
  • Nuclear and High Energy Physics 862
  • Computer Networks and Communications 1.2k
  • Aerospace Engineering 594
  • Computational Theory and Mathematics 340
Replace Toshiharu Sugie with:
Toshiharu Sugie Japan
A. Pironti Italy
G. De Tommasi Italy
R. Ambrosino Italy
B. Bandyopadhyay India
D.J.N. Limebeer United Kingdom
G. Stein United States
Min-Jea Tahk South Korea
M. Mattei Italy
Andrei Agrachev Italy
M. Ariola relative to Toshiharu Sugie Japan Toshiharu Sugie's profile →
Citations per field
00.5×1.5×
Toshiharu Sugie · 1×
Citations per year

Countries citing papers authored by M. Ariola

Since Specialization
Citations

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

Fields of papers citing papers by M. Ariola

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network

The 25 scholars most cited alongside M. Ariola, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.

Border = papers with M. Ariola Line = papers co-authored together M. Ariola links everyone, so they are left out of the graph.

All Works

20 of 20 papers shown
#Work
1 20250
2 20250
3 20240
4 20240
5 202312
6 20230
7 20217
8
DEMO control challenges
20201
9 201313
10 20126
11 200911
12 2009213
13 20072
14 20055
15 20033
16 20029
17 20023
18
Statistical-learning control of multiple-delay systems with application to ATM networks
20012
19 20019
20 199918

About M. Ariola

M. Ariola is a scholar working on Nuclear and High Energy Physics, Control and Systems Engineering, Aerospace Engineering, Numerical Analysis and Biomedical Engineering, having authored 150 papers that have together received 4.2k indexed citations. Recurring topics across this work include Magnetic confinement fusion research (64 papers), Stability and Control of Uncertain Systems (55 papers), Superconducting Materials and Applications (36 papers), Adaptive Control of Nonlinear Systems (23 papers), Fusion materials and technologies (22 papers), Advanced Control Systems Optimization (22 papers), Control Systems and Identification (18 papers) and Control and Stability of Dynamical Systems (12 papers). The work is most often cited by research in Control and Systems Engineering (3.0k citations), Nuclear and High Energy Physics (862 citations), Computer Networks and Communications (1.2k citations), Aerospace Engineering (594 citations) and Computational Theory and Mathematics (340 citations). M. Ariola has collaborated with scholars based in Italy, United Kingdom and United States. Frequent co-authors include Francesco Amato, Carlo Cosentino, P. Dorato, A. Pironti, R. Ambrosino, G. De Tommasi, G. Ambrosino, Gaetano Tartaglione, Chaouki T. Abdallah and F. Sartori. Their work appears in journals such as Fusion Engineering and Design, IET Control Theory and Applications, Automatica, IEEE Transactions on Automatic Control and IEEE Transactions on Control Systems Technology.

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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