A neural network-based input shaping for swing suppression of an overhead crane under payload hoisting and mass variations
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 pr...
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my.utm.859442020-07-30T07:39:05Z http://eprints.utm.my/id/eprint/85944/ A neural network-based input shaping for swing suppression of an overhead crane under payload hoisting and mass variations Ramli, Liyana Mohamed, Zaharuddin Jaafar, Hazriq Izzuan TK Electrical engineering. Electronics Nuclear engineering 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. Elsevier Ltd 2018-07 Article PeerReviewed 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 http://dx.doi.org/10.1016/j.ymssp.2018.01.029 |
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TK Electrical engineering. Electronics Nuclear engineering Ramli, Liyana Mohamed, Zaharuddin Jaafar, Hazriq Izzuan A neural network-based input shaping for swing suppression of an overhead crane under payload hoisting and mass variations |
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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. |
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Article |
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Ramli, Liyana Mohamed, Zaharuddin Jaafar, Hazriq Izzuan |
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Ramli, Liyana Mohamed, Zaharuddin Jaafar, Hazriq Izzuan |
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Ramli, Liyana |
title |
A neural network-based input shaping for swing suppression of an overhead crane under payload hoisting and mass variations |
title_short |
A neural network-based input shaping for swing suppression of an overhead crane under payload hoisting and mass variations |
title_full |
A neural network-based input shaping for swing suppression of an overhead crane under payload hoisting and mass variations |
title_fullStr |
A neural network-based input shaping for swing suppression of an overhead crane under payload hoisting and mass variations |
title_full_unstemmed |
A neural network-based input shaping for swing suppression of an overhead crane under payload hoisting and mass variations |
title_sort |
neural network-based input shaping for swing suppression of an overhead crane under payload hoisting and mass variations |
publisher |
Elsevier Ltd |
publishDate |
2018 |
url |
http://eprints.utm.my/id/eprint/85944/ http://dx.doi.org/10.1016/j.ymssp.2018.01.029 |
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1674066231391420416 |
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13.160551 |