Click Here for the List of Publications

For the most recent papers, watch out for my google scholar.

Manuscripts in Preparation

1. V. Renganathan, Probabilistic Robust Control Using High-Confidence Model-Ambiguity Sets , Journal Manuscript In Preparation, 2023.
2. V. Renganathan, Distributionally Robust Risk Allocation for Optimal Sampling Based Motion Planning Under Uncertainty , Journal Manuscript In Preparation, 2023.
3. V. Renganathan, N. Hashemi, J. V. Deshmukh, Data-driven Anomaly Detection Using Conformal Prediction , Conference Manuscript In Preparation, 2023.
4. V. Renganathan, A Novel Anticipatory Distributed Consensus Protocol for Multi-agent Systems , Journal Manuscript In Preparation, 2023.
5. M. Pfefferkorn, V. Renganathan, R. Findeisen, Regret & Sub-optimalty of Distributionally Robust Stochastic MPC.

Preprints in Submission

1. V. Renganathan, A. Rantzer, O. Kjellqvist, Distributed Adaptive Control For Uncertain Networks, Submitted for European Control Conference, 2024.

Journals

1. V. Renganathan, S. Safaoui, A. M. Kothari, B. Gravell, I. Shames, T. Summers, Risk Bounded Nonlinear Motion Planning With Integrated Perception & Control, Special Issue on Risk-aware Autonomous Systems: Theory and Practice, Artificial Intelligence, 2023.
2. V. Renganathan, B. Gravell, J.Ruths, T. Summers, Anomaly Detection Under Multiplicative Noise Model Uncertainty, IEEE Letters to Control Systems Society, 2022.
3. V. Renganathan, N. Hashemi, J. Ruths, T. Summers, Higher-Order Moment-Based Anomaly Detection, IEEE Letters to Control Systems Society, 2022.
4. V. Renganathan, K. Fathian, S. Safaoui, T. Summers, Spoof resilient coordination in distributed & robust robotic network, IEEE Transaction on Control Systems Technology, 2021.

Conferences

1. M. Pfefferkorn, V. Renganathan, R. Findeisen, Regret & Conservatism of Stochastic MPC, Accepted for IEEE ACC, 2024.
2. V. Renganathan, A. Iannelli, A. Rantzer, Online Learning Analysis for Minimax Adaptive Control, Accepted for IEEE CDC, 2023.
3. C. Alpturk, V. Renganathan, Risk Averse Path Planning Using Lipschitz Approximated Wasserstein Distributionally Robust Deep Q-learning, ECC, Bucharest, Romania, 2023.
4. V. Renganathan, A. Cervin, et.al, Learning-based Control and Estimation for Attitude Regulation of a Reusable Launcher for Landing Scenario, Accepted to the ESA-GNC & ICATT Conference, Sopot, Poland, 2023.
5. V. Renganathan, J. Pilipovsky, P. Tsoitras, Distributionally Robust Covariance Steering With Optimal Risk Allocation , Accepted to IEEE ACC, 2023.
6. K. Ekenberg, V. Renganathan, B. Olofsson, Distributionally Robust RRT with Risk Allocation , Accepted to IEEE ICRA, 2023.
7. T. Jouini, Z. Sun, V. Renganathan, Veit Hagenmeyer, Input and state constrained inverse optimal control with application to power networks , IFAC World Congress, 2023.
8. V. Renganathan, B. Gravell, J.Ruths, T. Summers, Anomaly Detection Under Multiplicative Noise Model Uncertainty , IEEE American Control Conference, Atlanta, USA 2022.
9. S. Safaoui, B. Gravell, V. Renganathan, T. Summers, Risk-Averse RRT* Planning with Nonlinear Steering and Tracking Controllers for Nonlinear Robotic Systems Under Uncertainty , IEEE IROS, 2021.
10. V. Renganathan, I. Shames, T. Summers, Towards Integrated Perception and Motion Planning with Distributionally Robust Risk Constraints , IFAC World Congress, 2020.
11. V. Renganathan, N. Hashemi, J. Ruths, T. Summers, Distributionally Robust Tuning of Anomaly Detectors in Cyber-Physical Systems with Stealthy Attacks , IEEE American Control Conference, 2020.
12. V. Raghuraman, V. Renganathan, T. Summers, J. Koeln, Hierarchical MPC with Coordinating Terminal Costs , IEEE American Control Conference, 2020.
13. V. Renganathan, T. Summers, Spoof Resilient Coordination for Distributed Multi-Robot Systems , Proceedings of The International Symposium on Multi-Robot and Multi-Agent Systems(MRS), Los Angeles, USA, Dec 4-5, 2017.