Modelling and control of helicopter robotic landing gear for uneven ground conditions

Boix, Daniel Melia; Goh, Keng; McWhinnie, James

Abstract

This paper proposes a method to investigate into helicopter landing on uneven terrain by means of using a scaled articulated robotic landing gear. A mathematical model of an articulated robotic landing gear that adapts to uneven ground conditions is considered. The model consists of a planar landing gear composed of two legs connected by a base and a skid at each end. Each skid has two degrees of freedom with PID joint controllers to provide stability while landing. A combination of Lagrange and Newton-Euler techniques is used to model the system dynamics. This work also includes a model of the ground interaction, a thrust controller and a level controller to maintain stability while landing. Experimental results with a laboratory-build scaled prototype are included and compared with the simulations.

Citation

Boix, D. M., Goh, K., & McWhinnie, J. (2017, October). Modelling and control of helicopter robotic landing gear for uneven ground conditions. Presented at 2017 Workshop on Research, Education and Development of Unmanned Aerial Systems (RED-UAS)

• Presentation Conference Type: Conference Paper (Published)
• Conference Name: 2017 Workshop on Research, Education and Development of Unmanned Aerial Systems (RED-UAS)
• Start Date: Oct 3, 2017
• End Date: Oct 5, 2017
• Acceptance Date: Sep 15, 2017
• Online Publication Date: Nov 9, 2017
• Publication Date: Nov 9, 2017
• Deposit Date: Sep 22, 2017
• Publicly Available Date: Sep 22, 2017
• Publisher: Institute of Electrical and Electronics Engineers
• Book Title: Proceedings of 2017 Workshop on Research, Education and Development of Unmanned Aerial Systems (RED-UAS 2017
• ISBN: 9781538609392
• DOI: https://doi.org/10.1109/RED-UAS.2017.8101644
• Keywords: PID control; robotic landing gear; nonlinear dynamic model; contact model; level controller; robot prototype
• Public URL: http://researchrepository.napier.ac.uk/Output/990344
• Contract Date: Sep 22, 2017