Neural Control and Online Learning for Speed Adaptation of Unmanned Aerial Vehicles.

ABSTRACT: Unmanned aerial vehicles (UAVs) are involved in critical tasks such as inspection and exploration. Thus, they have to perform several intelligent functions. Various control approaches have been proposed to implement these functions. Most classical UAV control approaches, such as model predictive control, require a dynamic model to determine the optimal control parameters. Other control approaches use machine learning techniques that require multiple learning trials to obtain the proper control parameters. All these approaches are computationally expensive. Our goal is to develop an efficient control system for UAVs that does not require a dynamic model and allows them to learn control parameters online with only a few trials and inexpensive computations. To achieve this, we developed a neu

SUBMITTER: Jaiton V 

PROVIDER: S-EPMC9082606 | biostudies-literature | 2022

REPOSITORIES: biostudies-literature