| On direct vs indirect data-driven predictive control V Krishnan, F Pasqualetti 2021 60th IEEE Conference on Decision and Control (CDC), 736-741, 2021 | 58 | 2021 |
| Formation control and trajectory tracking of nonholonomic mobile robots A Saradagi, V Muralidharan, V Krishnan, S Menta, AD Mahindrakar IEEE Transactions on Control Systems Technology 26 (6), 2250-2258, 2017 | 57 | 2017 |
| Distributed optimal transport for the deployment of swarms V Krishnan, S Martínez 2018 IEEE Conference on Decision and Control (CDC), 4583-4588, 2018 | 45 | 2018 |
| Distributed control for spatial self-organization of multi-agent swarms V Krishnan, S Martinez SIAM Journal on Control and Optimization 56 (5), 3642-3667, 2018 | 38 | 2018 |
| Lipschitz bounds and provably robust training by Laplacian smoothing V Krishnan, AARA Makdah, F Pasqualetti Advances in Neural Information Processing Systems 33, 10924-10935, 2020 | 25 | 2020 |
| Data-driven attack detection for linear systems V Krishnan, F Pasqualetti IEEE Control Systems Letters 5 (2), 671 - 676, 2020 | 21 | 2020 |
| Imitation and transfer learning for LQG control T Guo, AAR Al Makdah, V Krishnan, F Pasqualetti IEEE Control Systems Letters 7, 2149-2154, 2023 | 14 | 2023 |
| Behavioral feedback for optimal LQG control AAR Al Makdah, V Krishnan, V Katewa, F Pasqualetti 2022 IEEE 61st Conference on Decision and Control (CDC), 4660-4666, 2022 | 9 | 2022 |
| A probabilistic framework for moving-horizon estimation V Krishnan, S Martínez IEEE Transactions on Automatic Control 66 (4), 1817-1824, 2020 | 9 | 2020 |
| Self-organization in multi-agent swarms via distributed computation of diffeomorphisms V Krishnan, S Martínez Mathematical Theory of Networks and Systems, 706-713, 2016 | 9 | 2016 |
| Data-driven feedback linearization using the Koopman generator D Gadginmath, V Krishnan, F Pasqualetti arXiv preprint arXiv:2210.05046, 2022 | 7 | 2022 |
| A multiscale analysis of multi-agent coverage control algorithms V Krishnan, S Martínez Automatica 145, 110516, 2022 | 6 | 2022 |
| Identification of critical nodes in large-scale spatial networks V Krishnan, S Martínez IEEE Transactions on Control of Network Systems 6 (2), 842-851, 2018 | 6 | 2018 |
| Distributed online optimization for multi-agent optimal transport V Krishnan, S Martínez arXiv preprint arXiv:1804.01572, 2018 | 6 | 2018 |
| Direct vs indirect methods for behavior-based attack detection D Gadginmath, V Krishnan, F Pasqualetti 2022 IEEE 61st Conference on Decision and Control (CDC), 7090-7096, 2022 | 5 | 2022 |
| Learning Lipschitz feedback policies from expert demonstrations AAR Al Makdah, V Krishnan, F Pasqualetti IEEE Open Journal of Control Systems, 2022 | 5* | 2022 |
| Identification of critical node clusters for consensus in large-scale spatial networks V Krishnan, S Martínez IFAC-PapersOnLine 50 (1), 14156-14161, 2017 | 4 | 2017 |
| Optimal control of interacting active particles on complex landscapes S Sinha, V Krishnan, L Mahadevan arXiv preprint arXiv:2311.17039, 2023 | 1 | 2023 |
| Large-scale multi-agent transport: theory, algorithms and analysis V Krishnan University of California, San Diego, 2019 | 1 | 2019 |
| Optimal navigation of interacting active particles on complex landscapes V Krishnan, S Sinha, L Mahadevan Bulletin of the American Physical Society, 2024 | | 2024 |
Fabio PasqualettiProfessor, UC Riverside在 ucr.edu 的电子邮件经过验证
