Publications

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.

S. P. A. Datta, T. J. Saleem, M. Barati, M. V. López López, M.-L. Furgala, D. C. Vanegas, G. Santucci, P. P. Khargonekar, and E. S. McLamore, “Data, Analytics and Interoperability Between Systems (IoT) is Incongruous with the Economics of Technology,” pp. 7-88, Big Data Analytics for the Internet-of-Things, Eds. T. J. Saleem and M. A. Chishti, Wiley Data and , 2021.

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.

F. Lamnabhi-Lagarrigue, A. Annaswamy, S. Engell, A. Isaksson, P. Khargonekar, R. Murray, H. Nijmeijer, T. Samad, D. Tilbury, and P. Van den Hof, “Systems & Control for the Future of Humanity; Research agenda: Current and Future Roles, Impact and Grand Challenges,” Annual Reviews in Control, pp. 1-64, 2017.

Control and Machine Learning:

D. Muthirayan, D. Kalathil, and P. P. Khargonekar, “Meta-Learning Online Control for Linear Dynamical Systems,” IEEE Transactions on Automatic Control, 2025. DOI: 10.1109/TAC.2025.3536839

R. Du, D. Muthirayan, P. P. Khargonekar, and Y. Shen, “Long-term Fairness for Real-time Decision Making: A Constrained Online Optimization Approach,” IEEE Transactions on Neural Networks and Learning Systems, 2024. DOI: https://ieeexplore.ieee.org/document/10752738.

M. Rakka, M. E. Fouda, P. Khargonekar, and F. J. Kurdahi, “A Review of State-of-the-Art Mixed-Precision Neural Network Frameworks,” IEEE Transactions on Pattern Analysis and Machine Intelligence, pp. 1-20, 2024. DOI: 10.1109/TPAMI.2024.3394390

P. Khargonekar, “Imagined Futures: From Cybernetics to Cognitive Cyberphysical Systems,” IEEE Control Systems Magazine, pp. 9-11, August 2023. DOI: 10.1109/MCS.2023.3273494.

D. Muthirayan, R. Du, Y. Shen, and P. P. Khargonekar, “Change Point Detection Approach for Online Control of Unknown Time Varying Dynamical Systems,” Proc. 62nd IEEE Conference on Decision and Control, pp.5039-5046, 2023.

D. Muthirayan and P. P. Khargonekar, “Online Learning for Incentive-Based Demand Response,” Proc. 62nd IEEE Conference on Decision and Control, pp. 7956-7961, 2023.

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,” 20^th 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

S. Nivison and P. P. Khargonekar, “A Sparse Neural Network Approach to Model Reference Adaptive Control with Hypersonic Flight Applications,” Proc. 2018 AIAA Guidance, Navigation, and Control Conference, 2018.

S. Nivison and P. P. Khargonekar, “Improving Long-Term Learning of Model Reference Adaptive Controllers for Flight Applications: A Sparse Neural Network Approach,” Proc. 2017 AIAA Guidance, Navigation, and Control Conference, 2017.

S. Nivison and P. P. Khargonekar, “Development of a Robust Deep Recurrent Neural Network Controller for Flight Control Applications,” Proceedings American Control Conference, pp. 5336-5342, June 2017.

Renewable Energy, Smart Grids, Climate Change Mitigation, Adaptation and Resilience:

M. Netto, C. Emmanoulidis, and P. P. Khargonekar, “Systems and Control for Societal Impact: IFAC TC 9.2 Visions and UN SDGs,” IFAC-PapersOnLine, vol, 58, pp. 165-170, 2024.

P. P. Khargonekar and T. Samad, “The United Nations Sustainable Development Goals: An IFAC Agenda,” IFAC-PapersOnLine, vol, 58, pp. 153-158, 2024.

P. P. Khargonekar, T. Samad, S. Amin, A. Chakrabortty, F. Dabbene, A. Das, M. Fujita, M. Garcia-Sanz, D. F. Gayme, M. Ilić, I. Mareels, K. L. Moore, L. Y. Pao, A. Rajhans, J. Stoustrup, J. Zafar, and M. Bauer, Climate Change Mitigation, Adaptation, and Resilience: Challenges and Opportunities for the Control Systems Community, IEEE Control Systems Magazine, pp. 33-51, June 2024. DOI: 10.1109/MCS.2024.3382377

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.

P. Chakraborty, E. Baeyens and P. P. Khargonekar, “Grid Integration of Renewable Electricity and Distributed Control,” Emerging Applications of Control and Systems Theory, Springer, 2018.

R. Khatami, M. Parvania, P. P. Khargonekar, “Scheduling and Pricing of Energy Generation and Storage in Power Systems,” accepted for publication in IEEE Transactions on Power Systems.  

P. Chakaraborty, E. Baeyens, and P. P. Khargonekar, “Distributed Control of Flexible Demand using Proportional Allocation Mechanism in a Smart Grid: Game Theoretic Interaction and Price of Anarchy,”  Sustainable Energy, Grids and Networks, vol. 12, pp. 30-39, December 2017.

A. Bretas, N. G. Bretas, B. Carvalho, E. Baeyens, and P. P. Khargonekar, “Smart Grids Cyber-Physical Security as a Malicious Data Attack: An Innovation Approach,” Electric Power Systems Research, 2017.

P. Chakaraborty, E. Baeyens, and P. P. Khargonekar, “Cost Causation Based Allocation of Costs for Market Integration of Renewable Energy,” IEEE Transactions on Power Systems, 2017.

P. Chakraborty, E. Baeyens, P. P. Khargonekar, and K. Poolla, “A Cooperative Game for the Realized Profit of an Aggregation of Renewable Energy Producers,” Proc. 2016 IEEE Conference on Decision and Control, pp. 5805-5812, December 2016.

P. Chakraborty and P. P. Khargonekar, “A Demand Response Game and its Robust Price of Anarchy,” Proc. 2014 IEEE SmartGridComm, pp. 644-649, November 2014.

K. G. Nagananda and P. P. Khargonekar, “An Approximately Optimal Algorithm for Scheduling Phasor Data Transmissions in Smart Grid Networks,” IEEE Transactions on Smart Grid,  2017.

A. Ranjan, P. P. Khargonekar and S. Sahni, “Smart Grid Power Scheduling Via Bottom Left Decreasing Height Packing,” IEEE Symposium of Computers and Communications, pp. 1128-1133, 2016.

A. Ranjan, P. P. Khargonekar and S. Sahni, “Offline First Fit Decreasing Height Scheduling of Power Loads”, Journal of Scheduling, vol. 20, pp. 527-542, October 2017.

A. Ranjan, P. P. Khargonekar, and S. Sahni, “Offline Preemptive Scheduling of Power Demands to Minimize Peak Power in Smart Grids,” Proc. 19^th IEEE Symposium of Computers and Communications, Madeira, PORTUGAL, June 2014.

Adversarial Systems and Control:

Z. Fuchs and P. P. Khargonekar, “Generalized Engage or Retreat Differential Game with Escort Regions” IEEE Transactions on Automatic Control, pp. 668-681, 2017.

M. Park, K. Kalyanam, S. Darbha, P. P. Khargonekar, P. R. Chandler, and M. Pachter, “Performance Guarantee of an Approximate Dynamic Programming Policy for Robotic Surveillance,” IEEE Transactions on Automation Science and Engineering,  2016.

S. Rathinam and P. P. Khargonekar, “An Approximation Algorithm for the Shortest Path Dubins Problem,” Proc. ASME Dynamic Systems and Control Conference, 2016.

K. Kalyanam, S. Darbha, P. P. Khargonekar, M. Pachter, and P. Chandler, “Optimal Cooperative Pursuit on a Manhattan Grid,” Proc. 2013 AIAA Conference on Guidance, Navigation, and Control, 2013.

K. Kalyanam, S. Darbha, P. P. Khargonekar, D. Casbeer, M. Pachter, and P. Chandler, “Optimal Minimax Pursuit Evasion on a Manhattan Grid,” Proc. American Control Conference,  2013.

Neural Systems Modeling and Control:

S. Ratnadurai-Giridharan, P. P. Khargonekar, S. Talathi, “Emergent Gamma Synchrony in All-to-all Interneuronal Networks,” Frontiers in Computational Neuroscience, 2015.

M. Nandan, P. P. Khargonekar, and S. Talathi, “Fast SVM Training and Classification Using Approximate Extreme Points,” Journal of Machine Learning Research, vol. 15, pp. 59-98, 2014.

R. G. Shivakeshavan, R. A. Stefanescu, P. P. Khargonekar, P. R. Carney, and S. S. Talathi, “Genesis of Interictal Spikes in the CA1: A Computational Investigation,” Frontiers in Neural Circuits,  2014.

R. A. Stefanescu, R. G. Shivakeshavan, P. P. Khargonekar and S. S. Talathi, “Computational Modeling of Channelrhodopsin-2 Photocurrent Characteristics in Relation to Neural Signaling,” Bulletin of Mathematical Biology, vol. 75, pp. 2208-2240, 2013.

E. Boykin, P. P. Khargonekar, P. R. Carney, W. Ogle, and S. S. Talathi, “Detecting effective connectivity in networks of coupled neuronal oscillators,” Journal of Computational Neuroscience, DOI 10.1007/s10827-011-0367-3, vol. 32, pp. 521-538, 2012.

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:

P. P. Khargonekar, Professor R.E. Kalman–Reflections on his way of thinking, pp. 207-210, Annual Reviews in Control, 

 A. Antoulas, T. T Georgiou, P. P Khargonekar, A. B. Özgüler, E. D Sontag, Y. Yamamoto, “Obituary for Professor Rudolf Emil Kalman,” Automatica, vol. 76, pp. 370-371, 2016.

A. Antoulas, T. T. Georgiou, P. P Khargonekar, A. B. Ozguler, E. D. Sontag, and Y. Yamamoto, “Prof. Rudolf Emil Kalman [Obituary],” IEEE Control Systems Magazine, pp. 151-152, 2017.

A. Antoulas, T. T. Georgiou, P. P Khargonekar, A. B. Ozguler, E. D. Sontag, and Y. Yamamoto, “A Tribute to Rudolf Kalman: His Research, Life, and Influence,” IEEE Control Systems Magazine, p. 153, 2017.

P. P. Khargonekar, “Prof. R.E. Kalman-A Personal Tribute: My Debt to an Intellectual Giant,” IEEE Control Systems Magazine, pp. 171-172, 2017.

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)

P. P. Khargonekar, M. Vidyasagar – A Brilliant Intellect, Emerging Applications of Control and Systems Theory, Springer, pp. vii-xix, 2018.