Please visit my Google Scholar page for all publications (or see the list in the CV).
Selected recent publications:
Future Directions in Systems and Control:
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.
Renewable Energy and Smart Grids:
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. 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.
A. Ranjan, P. P. Khargonekar, and S. Sahni, “Offline Preemptive Scheduling of Power Demands to Minimize Peak Power in Smart Grids,” Proc. 19th IEEE Symposium of Computers and Communications, Madeira, PORTUGAL, June 2014.
Adversarial Systems and Control:
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.
Flight Control and Deep Learning:
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.
Neural Systems Modeling and Control:
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.
In Memory of Professor R. E. Kalman:
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, Foreword to the book entitled Cyber-Physical-Social Systems and Constructs in Electric Power Engineering, Siddharth Suryanarayanan, Robin Roche and Timothy M. Hansen (Eds)