Input shaping with an adaptive scheme for swing control of an underactuated tower crane under payload hoisting and mass variations

Mohamed, Zaharuddin and Fasih-Ur-Rehman, Syed Muhammad and Husain, Abdul Rashid and Abbasi, Muhammad Abbas and Jaafar, Hazriq Izzuan and Shaheed, Mohammad Hasan (2022) Input shaping with an adaptive scheme for swing control of an underactuated tower crane under payload hoisting and mass variations. Mechanical Systems and Signal Processing, 175. 01-16. ISSN 0888-3270

Text 1-S2.0-S0888327022002746-MAIN.PDF Restricted to Registered users only Download (7MB)

Abstract

An underactuated tower crane exhibits a significant payload sway especially under three simultaneous motions involving trolley displacement, jib rotation and payload hoisting. This paper proposes an input shaper with an adaptive scheme for payload swing control of a tower crane under those effects together with varying cable lengths for payload hoisting and various payload masses. The control approach has an advantage in its capability to adapt and update the Zero Vibration Derivative shaper parameters in real-time according to the changes in the system parameters. This is achieved by using a non-linear input–output mapping of the parameters developed using the neural network. To test the effectiveness of the proposed controller, experiments are carried out on a laboratory tower crane under several challenging conditions involving payload lowering and lifting operations, variation in speeds of motion, and using different payload masses up to ± 50% variations from an original mass. Experimental results show that the proposed shaper is robust towards the parameter uncertainties with low overall and residual payload sways under all testing conditions. Its superiority is confirmed by improvements of at least 49% and 76% in the payload lifting operation while 38% and 68% in the payload lowering operation for the overall and residual sways respectively over a robust Extra Insensitive shaper designed using an average operating frequency. In addition, the performance of the proposed controller is not affected by the variations in the payload masses and motion speeds.

Item Type:	Article
Uncontrolled Keywords:	Feed-forward control, Input shaping, Neural network, Swing control, Tower crane
Divisions:	Faculty of Electrical Engineering
Depositing User:	Sabariah Ismail
Date Deposited:	28 Mar 2023 15:11
Last Modified:	28 Mar 2023 15:11
URI:	http://eprints.utem.edu.my/id/eprint/26395
Statistic Details:	View Download Statistic

Actions (login required)

View Item