Hybrid control framework of uavs under varying wind and payload conditions
A Coursey, A Zhang… - 2024 American …, 2024 - ieeexplore.ieee.org
Reinforcement learning (RL) algorithms are increasingly applied to engineering control
applications. They offer a promising alternative to traditional control methods, which often …
applications. They offer a promising alternative to traditional control methods, which often …
Probabilistic Evaluation for Flight Mission Feasibility of a Small Octocopter in the Presence of Wind
View Video Presentation: https://doi. org/10.2514/6.2023-3964. vid The Advanced Air
Mobility (AAM) concept envisions small unmanned aerial systems (UASs) and some larger …
Mobility (AAM) concept envisions small unmanned aerial systems (UASs) and some larger …
Adaptive Learning of Design Strategies over Non-Hierarchical Multi-Fidelity Models via Policy Alignment
Multi-fidelity Reinforcement Learning (RL) frameworks significantly enhance the efficiency of
engineering design by leveraging analysis models with varying levels of accuracy and …
engineering design by leveraging analysis models with varying levels of accuracy and …
Quantifying the Sim-To-Real Gap in UAV Disturbance Rejection
Due to the safety risks and training sample inefficiency, it is often preferred to develop
controllers in simulation. However, minor differences between the simulation and the real …
controllers in simulation. However, minor differences between the simulation and the real …
A Reinforcement Learning Approach for Robust Supervisory Control of UAVs Under Disturbances
In this work, we present an approach to supervisory reinforcement learning control for
unmanned aerial vehicles (UAVs). UAVs are dynamic systems where control decisions in …
unmanned aerial vehicles (UAVs). UAVs are dynamic systems where control decisions in …
Flight Mission Feasibility Assessment of Urban Air Mobility Operations under Battery Energy Constraint
This paper introduces a decision-making framework for Urban Air Mobility (UAM) and
Unmanned Aerial Systems (UAS) operations that addresses the dual challenges of collision …
Unmanned Aerial Systems (UAS) operations that addresses the dual challenges of collision …
High-Fidelity Simulation of a Cartpole for Sim-to-Real Deep Reinforcement Learning
This work proposes a novel physics-based Cartpole simulation environment as a new
benchmark to address the sim-to-real transfer. Our simulation environment extends the …
benchmark to address the sim-to-real transfer. Our simulation environment extends the …
Robust trajectory planning for multi-rotor aerial vehicles subject to saturation faults and wind disturbances
View Video Presentation: https://doi. org/10.2514/6.2023-4041. vid In this paper, we propose
a trajectory planning approach to accommodate saturation faults associated with the …
a trajectory planning approach to accommodate saturation faults associated with the …
Adaptive Fault-tolerant Control Using Reinforcement Learning
I Ahmed - 2023 - search.proquest.com
Cyber-physical systems are ubiquitous in the modern world. They can be intricate and
diverse as they are prevalent. Such systems may operate with tight time-constants in the …
diverse as they are prevalent. Such systems may operate with tight time-constants in the …
Combining Reinforcement Learning and Cascade Pid Control for Uav Disturbance Rejection
Ensuring the safety of unmanned aerial vehicles (UAVs) under unknown disturbances, like
wind, is crucial due to their lightweight design. Traditional control-based approaches often …
wind, is crucial due to their lightweight design. Traditional control-based approaches often …