Universiti Teknologi Malaysia Institutional Repository

A neural network-based input shaping for swing suppression of an overhead crane under payload hoisting and mass variations

Ramli, Liyana and Mohamed, Zaharuddin and Jaafar, Hazriq Izzuan (2018) A neural network-based input shaping for swing suppression of an overhead crane under payload hoisting and mass variations. Mechanical Systems and Signal Processing, 107 . pp. 484-501. ISSN 0888-3270

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Official URL: http://dx.doi.org/10.1016/j.ymssp.2018.01.029

Abstract

This paper proposes an improved input shaping for minimising payload swing of an overhead crane with payload hoisting and payload mass variations. A real time unity magnitude zero vibration (UMZV) shaper is designed by using an artificial neural network trained by particle swarm optimisation. The proposed technique could predict and directly update the shaper's parameters in real time to handle the effects of time-varying parameters during the crane operation with hoisting. To evaluate the performances of the proposed method, experiments are conducted on a laboratory overhead crane with a payload hoisting, different payload masses and two different crane motions. The superiority of the proposed method is confirmed by reductions of at least 38.9% and 91.3% in the overall and residual swing responses, respectively over a UMZV shaper designed using an average operating frequency and a robust shaper namely Zero Vibration Derivative-Derivative (ZVDD). The proposed method also demonstrates a significant residual swing suppression as compared to a ZVDD shaper designed based on varying frequency. In addition, the significant reductions are achieved with a less shaper duration resulting in a satisfactory speed of response. It is envisaged that the proposed method can be used for designing effective input shapers for payload swing suppression of a crane with time-varying parameters and for a crane that employ finite actuation states.

Item Type:Article
Uncontrolled Keywords:Hoisting, Input shaping
Subjects:T Technology > TK Electrical engineering. Electronics Nuclear engineering
Divisions:Electrical Engineering
ID Code:85944
Deposited By: Widya Wahid
Deposited On:30 Jul 2020 15:39
Last Modified:30 Jul 2020 15:39

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