6 papers
Quadratic Characterizations for Reachability Analysis of Neural Networks
Elias Khalife, Mazen Farhood, Pierre-Loic Garoche
Quadratic constraints (QCs) are widely used to characterize nonlinearities and uncertainties, but generic analytical characterizations can be conservative on bounded domains. This…
Hypernetwork-Conditioned Reinforcement Learning for Robust Control of Fixed-Wing Aircraft under Actuator Failures
Dennis Marquis, Mazen Farhood
This paper presents a reinforcement learning-based path-following controller for a fixed-wing small uncrewed aircraft system (sUAS) that is robust to certain actuator failures. The…
Robust Control Design and Analysis Based on Lifting Linearization of Nonlinear Systems Under Uncertain Initial Conditions
Sourav Sinha, Mazen Farhood
This paper presents a robust control synthesis and analysis framework for nonlinear systems with uncertain initial conditions. First, a deep learning-based lifting approach is prop…
Adversarial Reinforcement Learning for Robust Control of Fixed-Wing Aircraft under Model Uncertainty
Dennis J. Marquis, Blake Wilhelm, Devaprakash Muniraj +1
This paper presents a reinforcement learning-based path-following controller for a fixed-wing small uncrewed aircraft system (sUAS) that is robust to uncertainties in the aerodynam…
Formally Proving Invariant Systemic Properties of Control Programs Using Ghost Code and Integral Quadratic Constraints
Elias Khalife, Pierre-Loic Garoche, Mazen Farhood
This paper focuses on formally verifying invariant properties of control programs both at the model and code levels. The physical process is described by an uncertain discrete-time…
Data-Driven Discrepancy Modeling in Higher-Dimensional State Space via Coprime Factorization
Sourav Sinha, Mazen Farhood
This work provides a data-driven framework that combines coprime factorization with a lifting linearization technique to model the discrepancy between a nonlinear system and its no…