Research

  • Intelligent control, nonlinear control
  • Control of multi-agents
  • Opinion dynamics
  • Smart sensors and actuators

Publications

Books:

  1. R. R. Selmic, V. Phoha, and A. Serwadda, Wireless Sensor Networks: Security, Coverage, and Localization, Springer, 2016.
  2. F. L. Lewis, J. Campos, and R. R. Selmic,
    Neuro-Fuzzy Control of Industrial Systems with Actuator Nonlinearities, SIAM Press, Philadelphia, PA, 2002.

Book Chapters:

  1. S. Ramazani, R. R. Selmic, and M. de Queiroz, “Multi-Agent Layered Formation Control Based on Rigid Graph Theory” in Control of Complex Systems: Theory and Applications, K. Vamvoudakis and J. Sarangapani (Eds.), Elsevier 2016, pp. 397-419.
  2. D. Jethwa, R. R. Selmic, and F. Figueroa, “Real-time implementation of intelligent actuator control with a transducer health monitoring capability,” in Recent Advances in Control Systems, Robotics, and Automation, S. Pennacchio (Ed.), Third Edition, Internationalsar, Italy, 2009.
  3. R. R. Selmic and F. L. Lewis, “Deadzone compensation in motion control systems using augmented multilayer neural networks,” in Adaptive Control of Systems with Nonsmooth Nonlinearities, G. Tao and F. L. Lewis (Eds.), Springer-Verlag, London, UK, 2001.
  4. R. R. Selmic and F. L. Lewis, “Neural network approximation of piecewise continuous functions: application to friction compensation,” in Soft Computing and Intelligent Systems: Theory and Applications, N. K. Sinha and M. M. Gupta (Eds.), Academic Press, London, UK, 2000.

Journal Papers:

  1. L. Badran, K. Aryankia, and R. R. Selmic, “Multi-agent consensus with non-commensurate time delay: Lambert W function approach,” IEEE Control Systems Letters (L-CSS), July 2025.
  2. A. M. M. Sizkouhi and R. R. Selmic, “Diff-VCA: A diffusion-based covert attack on hybrid adversary detection for autonomous vehicles,” submitted to the IEEE Transactions on Intelligent Vehicles, May 2025.
  3. M. Zareer and R. R. Selmic, “A survey on opinion dynamics in social media networks: Analysis, simulation, and control,” submitted to IEEE Transactions on Computational Social Systems, May 2025.
  4. M. Zareer and R. R. Selmic, “Maximizing opinion polarization using double deep Q-learning in social networks,” IEEE Access, vol. 13, January 2025. https://doi.org/10.1109/ACCESS.2025.3537397
  5. R. Babazadeh and R. R. Selmic, “Robust optimal distance-based formation control of uncertain nonlinear agents over directed topologies,” Asian Journal of Control, pp. 1-17, February 2025. https://doi.org/10.1002/asjc.3604
  6. M. Zareer and R. R. Selmic, “Modeling interactions in social media networks using an asynchronous and synchronous opinion dynamics,” Social Network Analysis and Mining, Springer Nature, vol. 14, no. 235, December 2024.
    https://doi.org/10.1007/s13278-024-01402-x
  7. M. Rahimifard and R. R. Selmic, “Zonotope-based leader-following consensus control and cyberattack detection in multiagent systems,” submitted to IEEE Transactions on Control of Network Systems, September 2024.
  8. A. M. M. Sizkouhi, M. Rahimifard, and R. R. Selmic, “A vision-based covert attack and hybrid adversary detection for autonomous vehicles using generative adversarial network,” submitted to the IEEE Transactions on Vehicular Technology, May 2024.
  9. M. Rahimifard, A. M. M. Sizkouhi, and R. R. Selmic, “Cyberattack detection for a class of nonlinear multi-agent systems using set-membership fuzzy filtering,” IEEE Systems Journal, vol. 18, no. 2, pp. 1056-1067, June 2024. https://doi.org/10.1109/JSYST.2024.3359427
  10. K. Aryankia and R. R. Selmic, “Robust adaptive leader-following formation control of nonlinear multi-agents using three-layer neural networks,” IEEE Transactions on Cybernetics, vol. 54, no. 10, pp. 5636-5648, October 2024. https://doi.org/10.1109/TCYB.2024.3356810
  11. K. Aryankia and R. R. Selmic, “Neuro-adaptive formation control of nonlinear multi-agent systems with communication delays,” Journal of Intelligent & Robotic Systems, vol. 109, no. 4, December 2023. https://doi.org/10.1007/s10846-023-02018-7
  12. M. Zareer and R. R. Selmic, “Modeling control agents in social media networks using reinforcement learning,” Special Issue on Innovation in Computing, Engineering Science & Technology in Advances in Science, Technology and Engineering Systems Journal (ASTESJ), vol. 8, no. 5, pp. 62-69, November 2023.
  13. R. R. Selmic, J. Scoggin, S. Oonk, and F. Maldonado, “Wireless sensor networks fault detection and identification,” International Journal of Robotics and Control Systems, vol. 3, no. 4, pp. 804-823, October 2023.
  14. N. Elhami Fard, R. R. Selmic, and K. Khorasani, “A review of techniques and policies on cybersecurity using artificial intelligence and reinforcement learning algorithms,” IEEE Technology and Society Magazine, vol. 42, no 3, pp. 57-68, September 2023. https://doi.org/10.1109/MTS.2023.3306540
  15. A. Mousavi and R. R. Selmic, “Wearable smart rings for multi-finger gesture recognition using supervised learning,” IEEE Transactions on Instrumentation and Measurement, vol. 72, pp. 1-12, August 2023. https://doi.org/10.1109/TIM.2023.3304703
  16. R. Babazadeh and R. R. Selmic, “Directed distance-based formation control of nonlinear heterogeneous agents in 3-D space,” IEEE Transactions on Aerospace and Electronic Systems, vol. 59, no. 3, pp. 3405-3415, June 2023. https://doi.org/10.1109/TAES.2022.3219039
  17. N. Elhami Fard, R. R. Selmic, and K. Khorasani, “Public policy challenges, regulations, oversight, technical, and ethical considerations for autonomous systems: A survey,” IEEE Technology and Society Magazine, vol. 42, no. 1, pp. 45-53, March 2023. https://doi.org/10.1109/MTS.2023.3241315
  18. N. Elhami Fard and R. R. Selmic, “Adversarial attacks on heterogeneous multi-agent deep reinforcement learning system with time-delayed data transmission,” Journal of Sensor and Actuator Networks, vol. 11, no. 3, September 2022. https://doi.org/10.3390/jsan11030045 (selected as a cover paper)
  19. S. A. Mousavi, K. Aryankia, and R. R. Selmic, “A distributed FDI cyber-attack detection in discrete-time nonlinear multi-agent systems using neural networks,” European Journal of Control, vol. 66, July 2022. https://doi.org/10.1016/j.ejcon.2022.100646
  20. P. Sadhukhan and R. R. Selmic, “Proximal policy optimization for formation navigation and obstacle avoidance,” International Journal of Intelligent Robotics and Applications, Springer, June 2022. https://doi.org/10.1007/s41315-022-00245-z
  21. N. Elhami Fard and R. R. Selmic, “Consensus of multi-agent reinforcement learning systems: The effect of immediate rewards,” Journal of Robotics and Control, vol. 3, no. 2, March 2022.
  22. E. F. M. Ferreira, J. Viana da Fonseca Neto, and R. R. Selmic, “HDP algorithms for trajectory tracking and formation control of multi-agent systems,” IEEE Access, March 2022. https://doi.org/10.1109/ACCESS.2022.3156092
  23. K. Aryankia and R. R. Selmic, “Neural network-based formation control with target tracking for second-order nonlinear multi-agent systems,” IEEE Transactions on Aerospace and Electronic Systems, vol. 58, no. 1, pp. 328-341, February 2022.
  24. K. Aryankia and R. R. Selmic, “Spectral properties of the normalized rigidity matrix for triangular formations,” IEEE Control Systems Letters (L-CSS), vol. 6, pp. 1154-1159, June 2021. https://doi.org/10.1109/LCSYS.2021.3089136
  25. K. Aryankia and R. R. Selmic, “Neuro-adaptive formation control and target tracking for nonlinear multi-agent systems with time-delay,” IEEE Control Systems Letters (L-CSS), vol. 5, no. 3, pp. 791-796, July 2021.
  26. K. Haratiannejadi and R. R. Selmic, “Smart Glove and Hand Gesture-based Control Interface for Multi-Rotor Aerial Vehicles in a Multi-Subject Environment,” IEEE Access, vol. 8, pp. 227667-227677, December 2020. https://doi.org/10.1109/ACCESS.2020.3045858
  27. R. Babazadeh and R. R. Selmic, “Distance-Based Multi-Agent Formation Control with Energy Constraints Using SDRE,” IEEE Transactions on Aerospace and Electronic Systems, vol. 56, no. 1, pp. 41-56, February 2020.
  28. A. Gardner, C. Duncan, J. Kanno, and R. Selmic, “On the Definiteness of Earth Mover’s Distance and Its Relation to Set Intersection,” IEEE Transactions on Cybernetics, vol. 48, no. 11, pp. 3184-3196, November 2018.
  29. S. Ramazani, R. R. Selmic, and M. S. DeQueiroz, “Rigidity-Based Multiagent Layered Formation Control,” IEEE Transactions on Cybernetics, vol. 47, no. 8, pp. 1902-1913, August 2017.
  30. S. Ramazani, J. Kanno, R. Selmic, and M. Brust, “Topological and Combinatorial Coverage Hole Detection in Coordinate-Free Wireless Sensor Networks,” International Journal of Sensor Networks, vol. 21, no. 1, pp. 40-52, January 2016.
  31. G. Zhang, C. Duncan, J. Kanno, and R. R. Selmic, “Unmanned ground vehicle navigation in coordinate-free and localization-free wireless sensor and actuator networks,” Journal of Intelligent and Robotic Systems, Springer, vol. 74, no. 3, pp. 869–891, June 2014.
  32. Y. B. Reddy and R. R. Selmic, “A trust-based approach for secure packet transfer in wireless sensor networks,” International Journal on Advances in Security, vol. 4, no. 3-4, December 2011.
  33. O. Kuljaca, J. Gadewadikar, and R. R. Selmic, “Adaptive Neural Network Frequency Control for Thermopower Generators,” International Journal of Robotics and Automation, vol. 26, no. 1, pp. 86-92, February 2011.
  34. R. Selmic, A. Mitra, S. Challa, and N. Simicevic, “Ultra-wideband Signal Propagation Experiments in Liquid Media,” IEEE Transactions on Instrumentation and Measurement, vol. 59, no. 1, pp. 215-220, January 2010.
  35. D. Jethwa, R. R. Selmic and F. Figueroa, “Real-time implementation of intelligent actuator control with a transducer health monitoring capability,” International Journal of Factory Automation, Robotics, and Soft Computing, no. 1, pp. 5–10, January 2009.
  36. A. I. Moustapha and R. R. Selmic, “Wireless sensor network modeling using modified recurrent neural networks: application to failure detection,” IEEE Transactions on Instrumentation and Measurement, vol. 57, no. 5, pp. 981-988, May 2008.
  37. R. Selmic, M. Polycarpou, and T. Parisini, “Actuator Fault Detection in Nonlinear Uncertain Systems Using Neural On-line Approximation Models,” European Journal of Control, vol. 15, no. 1, pp. 29-44, Jan-Feb 2009.
  38. W. Gao and R. R. Selmic, “Neural network control of a class of nonlinear systems with saturation,” IEEE Transactions on Neural Networks, vol. 17, no. 1, pp. 147-156, Jan. 2006.
  39. R. R. Selmic, book review for Automatica, vol. 42, no. 3, March 2006: Adaptive Control Design and Analysis, by Gang Tao, John Wiley & Sons, Inc., Hoboken, New Jersey, 640pp., 2003, ISBN: 0-471-27452-6.
  40. R. R. Selmic, “Discussion on a multi-model approach to failure detection in uncertain sampled-data systems,” European Journal of Control, vol. 11, pp. 266-268, November 2005.
  41. W. Zhou, A. Khaliq, Y. Tang, H.-F. Ji, and R. R. Selmic, “Simulation and design of piezoelectric microcantilever chemical sensors,” Sensors and Actuators A, vol. 125, no. 1, pp. 69-75, October 2005.
  42. R. K. Sunkam, J. S. Hill, R. R. Selmic, and D. T. Haynie, “Solid-state nanopulse generator: application in ultra-wideband bioeffects research,” Review of Scientific Instruments, vol. 76, 054702, May 2005.
  43. Javier Campos, Frank L. Lewis, and Rastko Selmic, “Backlash compensation with filtered prediction in discrete time nonlinear systems by dynamic inversion using neural networks,” Asian Journal of Control, vol. 6, no. 3, pp. 362-375, September 2004.
  44. S. K. Rangarajan, V. V. Phoha, K. Balagani, R. R. Selmic, S. S. Iyengar, “Web user clustering and its application to prefetching using ART neural networks,” IEEE Computer, April 2004.
  45. R. R. Selmic and F. L. Lewis, “Neural network approximation of piecewise continuous functions: application to friction compensation,” IEEE Transactions on Neural Networks, vol. 13, no. 3, pp. 745-751, May 2002.
  46. Rastko R. Selmic and Frank L. Lewis, “Neural net backlash compensation with Hebbian tuning using dynamic inversion,” Automatica, vol. 37, pp. 1269-1277, April 2001.
  47. Rastko R. Selmic and Frank L. Lewis, “Backlash compensation in nonlinear systems using dynamic inversion by neural networks,” Asian Journal of Control, vol. 2., no. 2, pp. 76-87, June 2000.
  48. Javier Campos, Frank L. Lewis, and Rastko Selmic, “Backlash compensation in discrete time nonlinear systems using dynamic inversion by neural networks: a preliminary approach,” Developments in Intelligent Control for Industrial Applications of the Int. Journal of Adaptive Control and Signal Processing, 1999.
  49. Rastko R. Selmic and Frank L. Lewis, “Deadzone compensation in motion control systems using neural networks,” IEEE Trans. Automat. Contr., vol. 45, no. 4, pp. 602-613, April 2000.
  50. F. L. Lewis, K. Liu, R. R. Selmic, and Li-Xin Wang, “Adaptive fuzzy logic compensation of actuator deadzones,” Journal of Robotic Systems, vol. 14, no. 6, pp. 501-511, 1997.

Conference Papers (last 5 years):

  1. L. Badran, K. Aryankia, and R. R. Selmic, “Multi-agent consensus with non-commensurate time delay: Lambert W function approach,” the 64th IEEE Conference on Decision and Control, Rio de Janeiro, Brazil, December 2025.
  2. J. F. N. Salik and R. R. Selmic, “Self-organizing agent formation based on population growth dynamics,” submitted to IEEE International Conference on Systems, Man, and Cybernetics (SMC), Vienna, Austria, October 2025.
  3. A. M. M. Sizkouhi and R. R. Selmic, “Vision-based covert attack and hybrid adversary detection for autonomous vehicles using generative networks,” submitted to IEEE International Conference on Systems, Man, and Cybernetics (SMC), Vienna, Austria, October 2025.
  4. N. Elhami Fard, B. Merikhi, and R. R. Selmic, “Cooperative consensus Q-learning for micro multi-agent tumor targeting,” submitted to IEEE International Conference on Systems, Man, and Cybernetics (SMC), Vienna, Austria, October 2025.
  5. M. Rahimifard and R. R. Selmic, “Resilient leader-following consensus control using set-membership fuzzy filtering,” submitted to the 64th IEEE Conference on Decision and Control, Rio de Janeiro, Brazil, December 2025.
  6. Z. Ebrahimi and R. R. Selmic, “Intelligent control systems for directional drilling: A GRU neural network approach,” Proc. the 33rd Mediterranean Conference on Control and Automation, Tangier, Morrocco, June 2025.
  7. M. Rahimifard and R. R. Selmic, “Zonotope-based cyberattack detection for leader-following multi-agent systems,” Proc. the 63rd IEEE Conference on Decision and Control, Milan, Italy, December 2024.
  8. N. Elhami Fard, B. Merikhi, R. R. Selmic, and R. McEwen, “Consensus control of micro multi-agent reinforcement learning systems for tumor treatment,” Proc. IEEE International Conference on Future Machine Learning and Data Science (FMLDS), Sydney, Australia, November 2024.
  9. M. Zareer and R. R. Selmic, “Maximizing disagreement and polarization in social media networks using double deep Q-learning,” Proc. IEEE International Conference on Systems, Man, and Cybernetics (SMC), Sarawak, Malaysia, October 2024.
  10. M. Krajicek-Allard, and R. R. Selmic, “Adaptive velocity control for a walking robot in low-traction environments,” Proc. the 18th IEEE International Conference on Control and Automation (ICCA), Reykjavk, Iceland, June 2024.
  11. R. Babazadeh, R. R. Selmic, and B. Fidan, “Allocation of formation control tasks using Delaunay triangulation and tetrahedralization,” Proc. the 32nd Mediterranean Confrence on Control and Automation, Chania, Crete, Greece, June 2024.
  12. M. Rahimifard, A. M. M. Sizkouhi, and R. R. Selmic, “Cyberattack detection for a class of nonlinear multi-agent systems using set-membership fuzzy filtering,” Proc. the 62nd IEEE Conference on Decision and Control, Marina Bay Sands, Singapore, December 2023.
  13. M. Zareer and R. R. Selmic, “Predicting opinions in social networks using recurrent neural networks,” Proc. the 31st Mediterranean Conference on Control and Automation, Limassol, Cyprus, June 2023.
  14. N. Elhami Fard and R. R. Selmic, “Modifying neural networks in adversarial agents of multi-agent reinforcement learning systems,” Proc. the 31st Mediterranean Conference on Control and Automation, Limassol, Cyprus, June 2023.
  15. K. Aryankia and R. R. Selmic, “Formation control for a class of nonlinear multi-agent systems using three-layer neural networks,” Proc. the 2023 American Control Conference, San Diego, CA, USA, June 2023.
  16. R. Babazadeh and R. R. Selmic, “Robust formation control of nonlinear agents with distance constraints,” Proc. the 61st IEEE Conference on Decision and Control, Cancun, Mexico, December 2022.
  17. M. Zareer and R. R. Selmic, “Modeling competing agents in social media networks,” Proc. the 17th International Conference on Control, Automation, Robotics and Vision (ICARCV), Singapore, December 2022.
  18. N. Elhami Fard and R. R. Selmic, “Data transmission resilience to cyber-attacks on heterogeneous multi-agent deep reinforcement learning systems,” Proc. 17th International Conference on Control, Automation, Robotics and Vision (ICARCV), Singapore, December 2022.
  19. A. M. M. Sizkouhi, M. Rahimifard, and R. R. Selmic, “Covert attack and detection through deep neural network on vision-based navigation systems of multi-agent autonomous vehicles,” Proc. IEEE International Conference on Systems, Man, and Cybernetics (SMC), Prague, Czech Republic, October 2022.
  20. N. Elhami Fard and R. R. Selmic, “Time-delayed Data Transmission in Heterogeneous Multi-agent Deep Reinforcement Learning System,” Proc. the 30th Mediterranean Conference on Control and Automation, Athens, Greece, June 2022.
  21. R. Babazadeh and R. R. Selmic, “Distance-based formation control of nonlinear agents over planar directed graphs,” Proc. the 2022 American Control Conference, Atlanta, Georgia, USA, June 2022.
  22. K. Aryankia and R. R. Selmic, “Spectral properties of the normalized rigidity matrix for triangular formations,” the 60th IEEE Conference on Decision and Control, Austin, Texas, USA, December 2021.
  23. M. Zareer and R. R. Selmic, “Expressed and Private Opinions Model with Asynchronous and Synchronous Updating,” Proc. IEEE International Conference on Systems, Man, and Cybernetics (SMC), Melbourne, Australia, October 2021.
  24. P. Sadhukhan and R. R. Selmic, “Multi-Agent Formation Control with Obstacle Avoidance Using Proximal Policy Optimization,” Proc. IEEE International Conference on Systems, Man, and Cybernetics (SMC), Melbourne, Australia, October 2021.
  25. S. A. Mousavi, K. Aryankia, and R. R. Selmic, “Cyber-attack detection in discrete nonlinear multi-agent systems using neural networks,” Proc. the 5th IEEE Conference on Control Technology and Applications (CCTA), San Diego, California, USA, August 2021.
  26. Ernesto F. M. Ferreira, J. Viana da Fonseca Neto, and R. R. Selmic, “Decentralized control for multi-agent system formation based on regular polygons,” 2020 IFAC World Congress, Berlin, Germany, July 2020.
  27. R. Babazadeh and R. R. Selmic, “Distance-based formation control over directed triangulated Laman graphs in 2-D space,” Proc. 59th IEEE Conference on Decision and Control, Jeju Island, Republic of Korea, December 2020.
  28. K. Aryankia and R. R. Selmic, “Neuro-adaptive formation control and target tracking for nonlinear multi-agent systems with time-delay,” the 59th IEEE Conference on Decision and Control, Jeju Island, Republic of Korea, December 2020.
  29. K. Aryankia and R. R. Selmic, “Formation control and target tracking for a class of nonlinear multi-agent systems using neural networks,” Proc. 2020 European Control Conference, Saint Petersburg, Russia, May 2020.
  30. R. Babazadeh, M. Roudneshin, R. R. Selmic, and A. G. Aghdam, “Robust suboptimal output synchronization of nonlinear heterogeneous agents,” Proc. the 2020 American Control Conference, Denver, CO, USA, July 2020.