Paper
17 May 2016 Comparison study of input shaping techniques to control an underactuated flexible link system
Yasser Al Hamidi, Micky Rakotondrabe
Author Affiliations +
Abstract
This paper compares between three different input shaping feedforward techniques, traditional (TIS), extra insensitive (EI), and modified input shaping (MIS), to reduce the vibration of a flexible link QUANSER system. The main challenge is that the system under test is an underactuated system: it has one input and two outputs. This makes the application of the input shaping techniques not utilizable directly. We therefore first propose to use a variable change at the output in order to make the process equivalent to a monovariable system without modification of the behavior and of the objective of the control. The experimental tests demonstrate the efficiency of the technique and the different results from the three control techniques are compared and discussed. It comes out that EI shapers are the most efficient in term of robustness. MIS shaper has a shorter length than that of a corresponding TIS shaper; however both shapers have the same ability of vibration suppression. Also MIS scheme is easier than the traditional scheme because the numerical optimization is unnecessary in the design of the MIS shaper. MIS shaper has an advantage over a TIS corresponding shaper in being capable of suppressing multimode of vibration.
© (2016) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Yasser Al Hamidi and Micky Rakotondrabe "Comparison study of input shaping techniques to control an underactuated flexible link system", Proc. SPIE 9859, Sensors for Next-Generation Robotics III, 985909 (17 May 2016); https://doi.org/10.1117/12.2228783
Lens.org Logo
CITATIONS
Cited by 1 scholarly publication.
Advertisement
Advertisement
RIGHTS & PERMISSIONS
Get copyright permission  Get copyright permission on Copyright Marketplace
KEYWORDS
Control systems

Motion models

Actuators

Sensors

Convolution

Feedback control

Systems modeling

Back to Top