Reinforcement learning-based controller design for a ‎proposed octorotor with tilt-arm angles ‎

Q: How can I download an article?

To download an article from SID, first log in to the site, search for the article title, and click on the 'Download Article' option.

Q: How can I download an ISI article?

To download an ISI article on SID, enter the keyword or article title in the search bar, view the relevant results, click on the desired article, and select the 'Download Article' option.

Q: How can I access the SID database?

To access the SID database, visit SID.ir, create an account, and log in to access scientific resources.

Q: Is downloading articles from SID free?

Some articles on SID are available for free, while others require payment. Details are specified on the article's page.

Sharifi Davoud; Irani Rahghi Mohsen; Torabi Keivan; Shahbazi Hamed

مرکز اطلاعات علمی Scientific Information Database (SID) - Trusted Source for Research and Academic Resources

Journal Paper

Paper Information

Journal: JOURNAL OF MECHANICAL ENGINEERING AMIRKABIR (AMIRKABIR) Year:2024 | Volume:55 | Issue:10 Page(s): 1175-1194

Download Full-Text

Persian Verion

View:

Download:

Cites:

Information Journal Paper

Title

Reinforcement learning-based controller design for a ‎proposed octorotor with tilt-arm angles ‎

Author(s)

Sharifi Davoud | Irani Rahghi Mohsen | Torabi Keivan | Shahbazi Hamed | Issue Writer Certificate

Keywords

Octorotor with tilt-arm angle

Reinforcement Learning

Trajectory tracking

Independent degrees of &lrm

freedom

Motor malfunctions

Abstract

The maneuverability of a quadrotor or octorotor UAV is limited in the standard configuration ‎because the force vectors of the propellers are parallel and only have four active degrees of ‎freedom. Therefore, they lack the controllability of six independent degrees of freedom. This ‎study designs a novel configuration for an octorotor capable of hovering with roll or pitch angles in ‎a specific position, contrary to UAVs with a standard configuration that can only hover in a ‎horizontal position. In other words, in this octorotor, orientation tracking is also added to the ‎octorotor's targets in addition to position tracking. The proposed model can be controlled by ‎altering the velocity of the eight rotors and the tilt angle of the four arms. Such alterations in ‎velocity and tilt angle are such that they can provide the aerial vehicle with most optimum ‎maneuverability. After deriving the proposed dynamic octorotor model, a controller is proposed ‎using neural networks (NNs) and Reinforcement Learning (RL), capable of controlling the proposed ‎octorotor with six independent degrees of freedom. Finally, Trajectory tracking, octorotor position, ‎and controller robustness to possible Motor malfunctions are examined, and numerical simulation ‎results are provided.‎

Multimedia

No record.

Cites

No record.

References

No record.

Cite

Related Journal Papers

No record.

Related Seminar Papers

No record.

Related Plans