Ritirupa Dey, Avimanyu Sahoo, and Vignesh Narayanan, "Safe data-enabled control of human-in-the-loop robotic manipulator systems," IEEE Transactions on Industrial Informatics, 2025, in press.
Vignesh Narayanan, Lawrence Bordoh, István Z. Kiss, and Jr-Shin Li, "Inferring networks of chemical reactions by curvature analysis of kinetic trajectories," Physical Chemistry Chemical Physics, The Royal Society of Chemistry, 2025 (Featured on the cover page).
Bharath Muppasani, Nitin Gupta, Vishal Pallagani, Biplav Srivastava, Raghava Mutharaju, Michael N. Huhns, and Vignesh Narayanan, "Building a planning ontology to represent and exploit planning knowledge and its applications," Discover Data, 2025, vol. 3(1): 55.
Yuan-Hung Kuan, Vignesh Narayanan, and Jr-Shin Li, "An iterative reservoir computing network approach to irregular time-series data reconstruction," IEEE Transactions on Neural Networks and Learning Systems, 2025, in press.
Vignesh Narayanan, Wei Zhang, and Jr-Shin Li, "Duality of ensemble systems through moment representations," IEEE Transactions on Automatic Control, vol. 69(10), pp. 7270-7276, Oct. 2024.
Chathurangi Shyalika, Kaushik Roy, Renjith Prasad, Fadi El Kalach, Yuxin Zi, Priya Mittal, Vignesh Narayanan, Ramy Harik, and Amit Sheth, "RI2AP: Robust and interpretable 2D anomaly prediction in assembly pipelines," Sensors, vol. 24(10), pp. 3244, 2024.
Maxwell Geiger, Vignesh Narayanan, Sarangapani Jagannathan, "Optimal trajectory tracking for uncertain linear discrete-time systems using time-varying Q-learning," International Journal of Adaptive Control and Signal Processing, vol. 38(7), pp. 2340-2368, 2024.
Yao-Chi Yu, Vignesh Narayanan, and Jr-Shin Li, "Moment-based reinforcement learning for ensemble control," IEEE Transactions on Neural Networks and Learning Systems, vol. 35(9), pp. 12653-12664, Sept. 2024.
Krishnan Raghavan, Vignesh Narayanan, and Sarangapani Jagannathan, "Cooperative deep Q-learning framework for environments providing image feedback," IEEE Transactions on Neural Networks and Learning Systems, vol. 35(7), pp. 9267-9276, July 2024.
Rohollah Moghadam, Vignesh Narayanan, and Sarangapani Jagannathan, "Event-triggered optimal adaptive control of partially unknown linear continuous-time systems with state delay," IEEE Systems, Man, and Cybernetics: Systems, vol. 53(6), pp. 3324-3337, June 2023.
Wei Miao, Vignesh Narayanan, and Jr-Shin Li, "Interpretable design of reservoir computing networks using realization theory," IEEE Transactions on Neural Networks and Learning Systems, vol. 34(9), pp. 6379-6389, Sept. 2023. [arxiv-pdf]
Vignesh Narayanan, Hamidreza Modares, Sarangapani Jagannathan, and Frank L. Lewis, "Event-driven off-policy reinforcement learning for control of interconnected systems," IEEE Transactions on Cybernetics, vol. 52(3), pp. 1936-1946, March 2022. [pdf-download]
Liang Wang, Vignesh Narayanan, Yao-Chi Yu, Yikyung Park, and Jr-Shin Li, "A two-stage hierarchical clustering method for structured temporal sequence data," Knowledge and Information Systems, vol. 63, pp. 1627–1662, 2021. [pdf]
Vignesh Narayanan, Hamidreza Modares, and Sarangapani Jagannathan, "Optimal event-triggered control of input-affine nonlinear interconnected systems using multi-player games," International Journal of Robust and Nonlinear Control, vol. 31, pp. 950–970, 2021. [pdf-download]
Wei-Cheng Jiang, Vignesh Narayanan, and Jr-Shin Li, "Model learning and knowledge sharing for cooperative multiagent systems in stochastic environment," IEEE Transactions on Cybernetics, vol. 51(12), pp. 5717-5727, Dec. 2021. [pdf]
Wei Miao, Vignesh Narayanan, and Jr-Shin Li, "Parallel residual projection: a new paradigm for solving linear inverse problems," Nature Scientific Reports, vol. 10(1), pp. 1-10, 2020. [pdf]
Avimanyu Sahoo and Vignesh Narayanan, "Differential-game for resource aware approximate optimal control of large-scale nonlinear systems with multiple players," Neural Networks, Elsevier, vol. 124, pp. 95-108, 2020. [pdf-download]
Vignesh Narayanan, Jr-Shin Li, and Shinung Ching, "Biophysically interpretable inference of single neuron dynamics," Journal of Computational Neuroscience, Springer, vol. 47(1), pp. 61-76, 2019. [pdf]
Avimanyu Sahoo and Vignesh Narayanan, "Optimization of sampling intervals for tracking control of nonlinear systems: A game theoretic approach," Neural Networks, Elsevier, vol. 114, pp. 78-90, 2019. [pdf-download]
Vignesh Narayanan, Avimanyu Sahoo, Sarangapani Jagannathan, and Koshy George, "Approximate optimal distributed control of nonlinear interconnected systems using event-triggered nonzero-sum games," IEEE Transactions on Neural Networks and Learning systems, vol. 30(5), pp. 1512-1522, 2018. [pdf-download]
Avimanyu Sahoo, Vignesh Narayanan, and Sarangapani Jagannathan, "A min–max approach to event- and self-triggered sampling and regulation of linear systems," IEEE Transactions on Industrial Electronics, vol. 66(7), pp. 5433-5440, 2018. [pdf-download]
Vignesh Narayanan, Sarangapani Jagannathan, and Ram Kumar, "Event-sampled output feedback control of robotic manipulators using neural networks," IEEE Transactions on Neural Networks and Learning Systems, vol. 30(6), pp. 1651-1658, 2017. [pdf-download]
Vignesh Narayanan and Sarangapani Jagannathan, "Event-triggered distributed control of nonlinear interconnected systems using online reinforcement learning with exploration," IEEE Transactions on Cybernetics, vol. 48(9), pp. 2510-2519, 2017. [pdf-download]
Vignesh Narayanan and Sarangapani Jagannathan, "Event-triggered distributed approximate optimal state and output control of affine nonlinear interconnected systems," IEEE Transactions on Neural Networks and Learning Systems, vol. 29(7), pp. 2846-2856, 2017. [pdf-download]
Nathan Szanto, Vignesh Narayanan, and Sarangapani Jagannathan, "Event-sampled direct adaptive neural network output- and state-feedback control of uncertain strict-feedback system," IEEE Transactions on Neural Networks and Learning Systems, vol. 29(5), pp. 1850-1863, 2017. [pdf-download]
Vignesh Narayanan and Sarangapani Jagannathan, "Distributed adaptive optimal regulation of uncertain large-scale interconnected systems using hybrid Q-learning approach," IET Control Theory and Applications, vol. 10(12), pp. 1448-1457, 2016. [pdf-download]