Please visit my Google Scholar page for all publications (or see the list in my CV).
Selected recent publications:
A. M. Annaswamy, P. P. Khargonekar, F. Lamnabhi-Lagarrigue, S. K. Spurgeon, (Editors), Cyber-Physical-Human Systems: Fundamentals and Applications, Wiley, 2023.
Societal Impacts of Technology:
P. P. Khargonekar and M. Sampath, “A Framework for Ethics of Cyber-Physical-Human Systems,” IFAC World Congress, July 2020.
M. Sampath and P. P. Khargonekar, “Socially Responsible Automation: A Framework for Shaping the Future“, National Academy of Engineering Bridge, vol. 48, no. 4, pp. 45-52, Winter 2018.
Future Directions in Systems and Control:
P. Khargonekar, T. Samad, S. Amin, A. Chakrabortty, F. Dabbene, A. Das, M. Fujita, M. Garcia-Sanz, D. Gayme,G. Hug, M. Ilić, I. Mareels, K. Moore, L. Y. Pao, A. Rajhans, J. Stoustrup, J. Zafar, and M. Bauer, “Climate Change Mitigation and Adaptation,” Chapter 2.A, in Control for Societal-Scale Challenges: Road Map 2030, pp. 7-22, IEEE Control Systems Society, Ed. A. Annaswamy, K. H. Johansson, and G. J. Pappas, May 2023.
P. P. Khargonekar and M. A. Dahleh, “Advancing Systems and Control Research in the Era of ML and AI,” Annual Reviews in Control, pp. 1-4, 2018.
Control and Machine Learning:
D. Muthirayan, C. Maheshwari, P. Khargonekar, and S. Sastry, Competing Bandits in Time Varying Matching Markets, Proc. Learning for Dynamics and Control Conference, 1020-1031, 2023.
S. M. Rodrigues, A. Kanduri, A. Nyamathi, N. Dutt, P. Khargonekar, and A. M. Rahmani, “Digital Health–Enabled Community-Centered Care: Scalable Model to Empower Future Community Health Workers Using Human-in-the-Loop Artificial Intelligence,” JMIR Form Res, 2022;6(4):e29535. DOI: 10.2196/29535
D. Muthirayan, J. Yuan, and P. P. Khargonekar, “Online Convex Optimization with Long Term Constraints for Predictable Sequences,” IEEE Control Systems Letters, 2023. DOI: 10.1109/LCSYS.2022.3230440
D. Muthirayan, J. Yuan, D. Kalathil, and P. P. Khargonekar, “Online Learning for Predictive Control with Provable Regret Guarantees,” Proceedings of IEEE 61st Conference on Decision and Control, pp. 6666-6671, 2022. DOI:10.1109/CDC51059.2022.9993300
D. Muthirayan, D. Kalathil, and P. P. Khargonekar, “Meta-learning online control for linear dynamical systems,” Proceedings of IEEE 61st Conference on Decision and Control, pp. 1435-1440, 2022. DOI: 10.1109/CDC51059.2022.9993222
D. Muthirayan and P. P. Khargonekar, “Memory Augmented Neural Network Adaptive Controllers: Performance and Stability”, IEEE Transactions on Automatic Control, 2022. DOI: 10.1109/TAC.2022.3144382
D. Muthirayan and P. P. Khargonekar, “Online Algorithms for Network Robustness under Connectivity Constraints,” IEEE Transactions on Network Science and Engineering, 2022. DOI: 10.1109/TNSE.2022.3160073
T. Mortlock, D. Muthirayan, S.-Y. Yu, P. P. Khargonekar, M. A. Al Faruque, “Graph Learning for Cognitive Digital Twins in Manufacturing Systems,” IEEE Transactions on Emerging Topics in Computing, 2022. DOI: 10.1109/TETC.2021.3132251
A. V. Malawade, S.-Y. Yu, B. Hsu, D. Muthirayan, P. P .Khargonekar, M. A. Al Faruque, “Spatio-Temporal Scene-Graph Embedding for Autonomous Vehicle Collision Prediction,” IEEE Internet of Things Journal, 2022. DOI: 10.1109/JIOT.2022.3141044.
S.-Y. Yu, A. V. Malawade, D. Muthirayan, P. P. Khargonekar, M. A. Al Faruque, “Scene-Graph Augmented Data-Driven Risk Assessment of Autonomous Vehicle Decisions,” IEEE Transactions on Intelligent Transportation Systems, 2021. DOI:10.1109/TITS.2021.3074854.
A. V. Malawade, N. D. Costa, D. Muthirayan, P. P. Khargonekar, and M. A. Al Faruque, “Neuroscience-Inspired Algorithms for the Predictive Maintenance of Manufacturing Systems,” IEEE Transactions on Industrial Informatics, vol. 17, pp. 7980 – 7990, 2021. DOI:10.1109/TII.2021.3062030
Y. Lin, P. Khargonekar, S. Mehrotra, and N. Venkatasubramanian, “T-cove: an exposure tracing system based on cleaning wi-fi events on organizational premises,” Proceedings of the VLDB Endowment, pp. 2783-2786, 2021. https://doi.org/10.14778/3476311.3476344
M. A. Al Faruque, D. Muthirayan, S.-Y. Yu, and P. P. Khargonekar, “Cognitive Digital Twin for Manufacturing Systems,” Design, Automation & Test in Europe Conference & Exhibition (DATE), 2021. DOI:10.23919/DATE51398.2021.9474166
A. Barua, D. Muthirayan, P. P. Khargonekar, and M. Al Faruque, “Hierarchical Temporal Memory based One-pass Learning for Real-Time Anomaly Detection and Simultaneous Data Prediction in Smart Grid,” IEEE Transactions on Dependable and Secure Computing, 2021.
D. Muthirayan and P. P. Khargonekar, Cognitive Preadaptation for Resilient Adaptive Control, AIAA Scitech 2021 Forum.
P. P. Khargonekar, “Cognitive Cyber-Physical Systems: Cognitive Neuroscience, Machine Learning, and Control,” Extended Abstract for an Invited Presentation at American Control Conference, July 2020.
D. Muthirayan, S. Nivison, P. P. Khargonekar, “Improved Attention Models for Memory Augmented Neural Network Adaptive Controllers“, Proceedings of American Control Conference, July 2020.
A. Barua, D. Muthirayan, P. P. Khargonekar, and M. Al Faruque, “Hierarchical Temporal Memory Based Machine Learning for Real-Time, Unsupervised Anomaly Detection in Smart Grid: WiP Abstract,” 20th ACM/IEEE International Conference on Cyber Physical Systems (WiP, Posters, and Demos), pp. 188-189, 2020.
D. Muthirayan and P. P. Khargonekar, “Memory Augmented Neural Network Adaptive Controllers: Performance and Stability,” submitted for publication, 2019.
S. Nivison and P. P. Khargonekar, “Development of a Robust, Sparsely-Activated, and Deep Recurrent Neural Network Controller for Flight Applications,” Proc. IEEE Conference on Decision and Control, pp. 384-390, December 2018. DOI: https://ieeexplore.ieee.org/abstract/document/8619188
Renewable Energy and Smart Grids:
V. S. M. Babu K., P. Chakraborty, E. Baeyens, P. P. Khargonekar, “Optimal Storage and Solar Capacity of a Residential Household Under Net Metering and Time-of-Use Pricing,” IEEE Control Systems Letters, 2023. DOI: 10.1109/LCSYS.2022.3232652
M. Majidi, D. Muthirayan, M. Parvania, and P. P. Khargonekar, “On the Performance of Reinforcement Learning Algorithms for Dynamic Matching of Renewable Energy with Flexible Loads,” Proceedings of IEEE 61st Conference on Decision and Control, pp. 6344-6349, 2022. DOI: 10.1109/CDC51059.2022.9992596
Y-J. Son, S.-H. Lim, S.-G. Yoon and P. Khargonekar, “Residential Demand Response based Load-Shifting Scheme to Increase Hosting Capacity in Distribution System,” IEEE Access, 2022. DOI: 10.1109/ACCESS.2022.3151172
A. Ranjan, P. Khargonekar, and S. Sahni, “Offline preemptive bottom left decreasing height scheduling of power loads in smart grids,” Energy Systems, pp. 1-26, 2021. https://doi.org/10.1007/s12667-021-00453-9.
D. Muthirayan, M. Parvania, and P. P. Khargonekar, “Online Algorithms for Dynamic Matching Markets in Power Distribution Systems,” IEEE Control Systems Letters, pp. , 2020. DOI: 10.1109/LCSYS.2020.3008084
D. Muthirayan, E. Baeyens, P. Chakraborty, K. Poolla, P. P. Khargonekar, “A minimal incentive-based demand response program with self reported baseline mechanism,” IEEE Transactions on Smart Grid, 2019.
H. T. Nguyen, M. Parvania, P. P. Khargonekar, “Worst-Case Probabilistic Network Outage Identification Under Physical Disturbances,” IEEE Control Systems Letters, 2019.
N. Aguiar, V. Gupta, P. P. Khargonekar, “A real options market-based approach to increase penetration of renewables,” IEEE Transactions on Smart Grid, 2019.
R. Khatami, M. Parvania, and P. P. Khargonekar, “Continuous-time Look-Ahead Scheduling of Energy Storage in Regulation Markets,” Proceedings of the 52nd Hawaii International Conference on System Sciences, January 2019. http://hdl.handle.net/10125/59796
R. Khatami, M. Parvania, P. P. Khargonekar and A. Narayanan, “Continuous-Time Stochastic Modeling and Estimation of Electricity Load, Proc. IEEE Conference on Decision and Control, pp. 3988-3993, December 2018. DOI: https://ieeexplore.ieee.org/abstract/document/8619042
E. Bitar, P. P. Khargonekar, and K. Poolla, “On the Marginal Value of Electricity Storage,” Systems and Control Letters, vol. 123, pp. 151-159, 2019.
R. Khatami, M. Heidarifar, M. Parvania, and P. P. Khargonekar, “Scheduling and Pricing of Load Flexibility in Power Systems,” IEEE Journal on Selected Topics in Signal Processing, August 2018.
Adversarial Systems and Control:
Neural Systems Modeling and Control:
S. Talathi, P. R. Carney, and P. P. Khargonekar, “Control of Neural Synchrony Using Channelrhodopsin-2: A Computational Study,” Journal of Computational Neuroscience, vol.31, pp. 87-103, DOI: 10.1007/s10827-010-0296-6, 2011.
In Memory of Professor R. E. Kalman:
Essays:
P. P. Khargonekar, Foreword to the book entitled Cyber-Physical-Social Systems and Constructs in Electric Power Engineering, Siddharth Suryanarayanan, Robin Roche and Timothy M. Hansen (Eds)