Master Thesis

  • Omar Qasem. Observer Design for LPV systems with One-Sided Lipschitz Nonlinearities and Disturbances. Kuwait University, 2018.

Journal Articles

  • Ali Abdullah, Omar Qasem, Full-order and reduced-order observers for linear parameter-varying systems with one-sided Lipschitz nonlinearities and disturbances using parameter-dependent Lyapunov function, Journal of the Franklin Institute, Volume 356, Issue 10, 2019, Pages 5541-5572, ISSN 0016-0032,
  • O. Qasem, W. Gao, K. G. Vamvoudakis, “Adaptive Optimal Control of Continuous-Time Nonlinear Systems via Hybrid Iteration,” submitted, 2022.
  • O. Qasem, M. Davari, W. Gao, Daniel R. Kirk, and Tianyou Chai. “Hybrid Iteration ADP Algorithm to Solve the Cooperative, Optimal Output Regulation Problem for Continuous-Time, Linear, Multi-Agent Systems: Theory and Application in Islanded Modern Microgrids With IBRs”, in IEEE Transactions on Industrial Electronics.
  • O. Qasem, and W. Gao, “Robust Policy Iteration of Uncertain Interconnected Systems with Imperfect Data.”, submitted 2022.
  • O. Qasem, H. Gutierrez and W. Gao, “Experimental Validation of Data-Driven Adaptive Optimal Control for Continuous-Time Systems via Hybrid Iteration”, submitted, 2023.

Conference Papers

  • O. Qasem, W. Gao and T. Bian, “Adaptive Optimal Control of Continuous-Time Linear Systems via Hybrid Iteration,” 2021 IEEE Symposium Series on Computational Intelligence (SSCI), 2021, pp. 01-07, doi: 10.1109/SSCI50451.2021.9660016.
  • O. Qasem, K. Jebari and W. Gao, “Adaptive Dynamic Programming and Data-Driven Cooperative Optimal Output Regulation with Adaptive Observers”, Conference on Decision and Control (CDC), 2022, pp. 2538-2543.
  • O. Qasem, M. Tiwari and H. Gutierrez, “Autonomous Satellite Docking via Adaptive Optimal Output Regulation: A Reinforcement Learning Approach”, 33rd AAS/AIAA Space Flight Mechanics Meeting, 2023, Austin, Texas. Accepted, in press.
  • O. Qasem, W. Gao and H. Gutierrez, “Adaptive Optimal Control for Discrete-Time Linear Systems via Hybrid Iteration”, 2023 IEEE 12th Data Driven Control and Learning Systems Conference.