5 papers
Online Reinforcement Learning for Safe Gain Scheduling in Nonlinear Quadrotor Control
Muhammad Junayed Hasan Zahed, Chieh Tsai, Salim Hariri +1
This paper presents an online reinforcement-learning framework for safe gain scheduling of a nonlinear quadcopter controller. Rather than learning thrust and torque commands direct…
Learning over Forward-Invariant Policy Classes: Reinforcement Learning without Safety Concerns
Chieh Tsai, Muhammad Junayed Hasan Zahed, Salim Hariri +1
This paper proposes a safe reinforcement learning (RL) framework based on forward-invariance-induced action-space design. The control problem is cast as a Markov decision process,…
Deep Q-Learning-Based Gain Scheduling for Nonlinear Quadcopter Dynamics
Hossein Rastgoftar, Muhammad J. H. Zahed
This paper presents a deep Q-network (DQN)-based gain-scheduling framework for safety-critical quadcopter trajectory tracking. Instead of directly learning control inputs, the prop…
Deep Neural Network-Based Aerial Transport in the Presence of Cooperative and Uncooperative UAS
Muhammad Junayed Hasan Zahed, Hossein Rastgoftar
We present a resilient deep neural network (DNN) framework for decentralized transport and coverage using uncrewed aerial systems (UAS) operating in . The proposed DN…
A Physics-Informed Fixed Skyroad Model for Continuous UAS Traffic Management (C-UTM)
Muhammad Junayed Hasan Zahed, Hossein Rastgoftar
Unlike traditional multi-agent coordination frameworks, which assume a fixed number of agents, UAS traffic management (UTM) requires a platform that enables Uncrewed Aerial Systems…