Moad Abudia

According to our database, Moad Abudia authored at least 11 papers between 2020 and 2024.

Collaborative distances:

• Dijkstra number of five.
• Erdős number of four.

2024

Dynamic Mode Decomposition of Control-Affine Nonlinear Systems Using Discrete Control Liouville Operators.

Zachary Morrison, Moad Abudia, Joel A. Rosenfeld, Rushikesh Kamalapurkar

IEEE Control. Syst. Lett., 2024

2023

Carleman Lifting for Nonlinear System Identification with Guaranteed Error Bounds.

Moad Abudia, Joel A. Rosenfeld, Rushikesh Kamalapurkar

Proceedings of the American Control Conference, 2023

2022

Safe Controller for Output Feedback Linear Systems using Model-Based Reinforcement Learning.

S. M. Nahid Mahmud, Moad Abudia, Scott A. Nivison, Zachary I. Bell, Rushikesh Kamalapurkar

CoRR, 2022

Model-based inverse reinforcement learning for deterministic systems.

Ryan Self, Moad Abudia, S. M. Nahid Mahmud, Rushikesh Kamalapurkar

Autom., 2022

2021

Safety aware model-based reinforcement learning for optimal control of a class of output-feedback nonlinear systems.

S. M. Nahid Mahmud, Moad Abudia, Scott A. Nivison, Zachary I. Bell, Rushikesh Kamalapurkar

CoRR, 2021

Anti-Koopmanism.

Efrain Gonzalez, Moad Abudia, Michael Jury, Rushikesh Kamalapurkar, Joel A. Rosenfeld

CoRR, 2021

Control Occupation Kernel Regression for Nonlinear Control-Affine Systems.

Moad Abudia, Tejasvi Channagiri, Joel A. Rosenfeld, Rushikesh Kamalapurkar

CoRR, 2021

2020

Online inverse reinforcement learning with unknown disturbances.

Ryan Self, Moad Abudia, Rushikesh Kamalapurkar

CoRR, 2020

Model-Based Reinforcement Learning for Optimal Feedback Control of Switched Systems.

Max L. Greene, Moad Abudia, Rushikesh Kamalapurkar, Warren E. Dixon

Proceedings of the 59th IEEE Conference on Decision and Control, 2020

Switched Optimal Control and Dwell Time Constraints: A Preliminary Study.

Moad Abudia, Michael Harlan, Ryan Self, Rushikesh Kamalapurkar

Proceedings of the 59th IEEE Conference on Decision and Control, 2020

Online inverse reinforcement learning for systems with disturbances.

Ryan Self, Moad Abudia, Rushikesh Kamalapurkar

Proceedings of the 2020 American Control Conference, 2020