Rushikesh Kamalapurkar

2.8k total citations · 1 hit paper
72 papers, 2.0k citations indexed

About

Rushikesh Kamalapurkar is a scholar working on Control and Systems Engineering, Computational Theory and Mathematics and Artificial Intelligence. According to data from OpenAlex, Rushikesh Kamalapurkar has authored 72 papers receiving a total of 2.0k indexed citations (citations by other indexed papers that have themselves been cited), including 40 papers in Control and Systems Engineering, 38 papers in Computational Theory and Mathematics and 27 papers in Artificial Intelligence. Recurrent topics in Rushikesh Kamalapurkar's work include Adaptive Dynamic Programming Control (37 papers), Adaptive Control of Nonlinear Systems (22 papers) and Reinforcement Learning in Robotics (21 papers). Rushikesh Kamalapurkar is often cited by papers focused on Adaptive Dynamic Programming Control (37 papers), Adaptive Control of Nonlinear Systems (22 papers) and Reinforcement Learning in Robotics (21 papers). Rushikesh Kamalapurkar collaborates with scholars based in United States, India and Vietnam. Rushikesh Kamalapurkar's co-authors include Warren E. Dixon, Shubhendu Bhasin, Patrick Walters, M. Johnson, Frank L. Lewis, Kyriakos G. Vamvoudakis, Joel A. Rosenfeld, Huyen T. Dinh, Nicholas Fischer and Justin R. Klotz and has published in prestigious journals such as IEEE Transactions on Automatic Control, Automatica and Energy and Buildings.

In The Last Decade

Rushikesh Kamalapurkar

62 papers receiving 1.9k citations

Hit Papers

A novel actor–critic–identifier architecture for approxim... 2012 2026 2016 2021 2012 100 200 300 400

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Rushikesh Kamalapurkar United States 20 1.3k 1.2k 711 306 286 72 2.0k
Shubhendu Bhasin India 22 1.4k 1.1× 961 0.8× 493 0.7× 301 1.0× 296 1.0× 96 2.0k
Yu Jiang China 21 1.6k 1.2× 2.0k 1.6× 832 1.2× 645 2.1× 793 2.8× 72 2.8k
Travis Dierks United States 19 1.5k 1.2× 1.0k 0.8× 447 0.6× 241 0.8× 256 0.9× 40 2.0k
Lu Dong China 19 782 0.6× 520 0.4× 432 0.6× 160 0.5× 311 1.1× 75 1.5k
Hao Xu United States 24 1.2k 0.9× 737 0.6× 381 0.5× 137 0.4× 623 2.2× 136 2.0k
Pengfei Yan China 16 364 0.3× 433 0.3× 254 0.4× 135 0.4× 200 0.7× 50 1.1k
Yiting Dong United States 11 1.7k 1.4× 477 0.4× 237 0.3× 379 1.2× 142 0.5× 21 2.1k
Dapeng Li China 22 2.0k 1.6× 811 0.7× 263 0.4× 85 0.3× 159 0.6× 58 2.5k
Jin Bae Park South Korea 23 1.2k 0.9× 274 0.2× 324 0.5× 114 0.4× 437 1.5× 124 1.7k
Hui Ma China 29 2.9k 2.3× 955 0.8× 372 0.5× 73 0.2× 268 0.9× 84 3.7k

Countries citing papers authored by Rushikesh Kamalapurkar

Since Specialization
Citations

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

Fields of papers citing papers by Rushikesh Kamalapurkar

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Rushikesh Kamalapurkar

This figure shows the co-authorship network connecting the top 25 collaborators of Rushikesh Kamalapurkar. A scholar is included among the top collaborators of Rushikesh Kamalapurkar 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 Rushikesh Kamalapurkar. Rushikesh Kamalapurkar 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.
Bradshaw, Craig R., et al.. (2025). Development and performance evaluation of empirical models compatible with building energy modeling engines for unitary equipment. Science and Technology for the Built Environment. 31(5). 533–550. 2 indexed citations
2.
Patil, Omkar Sudhir, Rushikesh Kamalapurkar, & Warren E. Dixon. (2025). Saturated RISE Controllers With Exponential Stability Guarantees: A Projected Dynamical Systems Approach. IEEE Transactions on Automatic Control. 70(7). 4936–4942.
3.
Bradshaw, Craig R., et al.. (2024). A gray-box model for unitary air conditioners developed with symbolic regression. International Journal of Refrigeration. 168. 696–707. 11 indexed citations
4.
Rosenfeld, Joel A., et al.. (2024). On Convergent Dynamic Mode Decomposition and its Equivalence with Occupation Kernel Regression. IFAC-PapersOnLine. 58(17). 103–108.
5.
Rosenfeld, Joel A. & Rushikesh Kamalapurkar. (2024). Dynamic Mode Decomposition With Control Liouville Operators. IEEE Transactions on Automatic Control. 69(12). 8571–8586. 2 indexed citations
6.
Kamalapurkar, Rushikesh, et al.. (2024). Approximate Dynamic Programming for Trajectory Tracking of Switched Systems. IEEE Transactions on Automatic Control. 70(2). 1024–1037.
7.
Rosenfeld, Joel A., et al.. (2024). The Occupation Kernel Method for Nonlinear System Identification. SIAM Journal on Control and Optimization. 62(3). 1643–1668. 6 indexed citations
8.
Kamalapurkar, Rushikesh, et al.. (2024). Safe adaptive output‐feedback optimal control of a class of linear systems. International Journal of Robust and Nonlinear Control. 34(11). 7082–7095.
9.
Rosenfeld, Joel A. & Rushikesh Kamalapurkar. (2023). Singular Dynamic Mode Decomposition. SIAM Journal on Applied Dynamical Systems. 22(3). 2357–2381. 4 indexed citations
10.
Rosenfeld, Joel A., et al.. (2023). Dynamic Mode Decomposition of Control-Affine Nonlinear Systems Using Discrete Control Liouville Operators. IEEE Control Systems Letters. 8. 79–84.
11.
Kamalapurkar, Rushikesh, et al.. (2023). Fault Detection via Occupation Kernel Principal Component Analysis. IEEE Control Systems Letters. 7. 2695–2700.
13.
Kamalapurkar, Rushikesh, et al.. (2022). Model-based inverse reinforcement learning for deterministic systems. Automatica. 140. 110242–110242. 30 indexed citations
14.
Rosenfeld, Joel A., et al.. (2021). Dynamic Mode Decomposition for Continuous Time Systems with the Liouville Operator. Journal of Nonlinear Science. 32(1). 17 indexed citations
15.
Rosenfeld, Joel A. & Rushikesh Kamalapurkar. (2021). Dynamic Mode Decomposition with Control Liouville Operators. IFAC-PapersOnLine. 54(9). 707–712. 10 indexed citations
16.
Rosenfeld, Joel A., Rushikesh Kamalapurkar, & Warren E. Dixon. (2018). The State Following Approximation Method. IEEE Transactions on Neural Networks and Learning Systems. 30(6). 1716–1730. 14 indexed citations
17.
Rosenfeld, Joel A., et al.. (2018). Approximate Dynamic Programming: Combining Regional and Local State Following Approximations. IEEE Transactions on Neural Networks and Learning Systems. 29(6). 2154–2166. 22 indexed citations
18.
Parikh, Anup, Rushikesh Kamalapurkar, & Warren E. Dixon. (2018). Target Tracking in the Presence of Intermittent Measurements via Motion Model Learning. IEEE Transactions on Robotics. 34(3). 805–819. 27 indexed citations
19.
Parikh, Anup, Rushikesh Kamalapurkar, Hsi‐Yuan Chen, & Warren E. Dixon. (2015). Homography based visual servo control with scene reconstruction. 6972–6977. 13 indexed citations
20.
Rosenfeld, Joel A., Rushikesh Kamalapurkar, & Warren E. Dixon. (2015). State following (StaF) kernel functions for function approximation Part I: Theory and motivation. 1217–1222. 9 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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