Payload swing control of a tower crane using a neural network–based input shaper
This paper proposes an input shaping technique for efficient payload swing control of a tower crane with cable length variations. Artificial neural network is utilized to design a zero vibration derivative shaper that can be updated according to different cable lengths as the natural frequency and d...
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| Format: | Article |
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SAGE Publications Ltd
2020
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| _version_ | 1796864939239931904 |
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| author | Fasih, S. M. Mohamed, Zaharuddin Husain, Abdul Rashid Ramli, Liyana Abdullahi, Auwalu Anjum, Waqas |
| author_facet | Fasih, S. M. Mohamed, Zaharuddin Husain, Abdul Rashid Ramli, Liyana Abdullahi, Auwalu Anjum, Waqas |
| author_sort | Fasih, S. M. |
| collection | ePrints |
| description | This paper proposes an input shaping technique for efficient payload swing control of a tower crane with cable length variations. Artificial neural network is utilized to design a zero vibration derivative shaper that can be updated according to different cable lengths as the natural frequency and damping ratio of the system changes. Unlike the conventional input shapers that are designed based on a fixed frequency, the proposed technique can predict and update the optimal shaper parameters according to the new cable length and natural frequency. Performance of the proposed technique is evaluated by conducting experiments on a laboratory tower crane with cable length variations and under simultaneous tangential and radial crane motions. The shaper is shown to be robust and provides low payload oscillation with up to 40% variations in the natural frequency. With a 40% decrease in the natural frequency, the superiority of the artificial neural network–zero vibration derivative shaper is confirmed by achieving at least a 50% reduction in the overall and residual payload oscillations when compared to the robust zero vibration derivative and extra insensitive shapers designed based on the average operating frequency. It is envisaged that the proposed shaper can be further utilized for control of tower cranes with more parameter uncertainties. |
| first_indexed | 2024-03-05T20:49:25Z |
| format | Article |
| id | utm.eprints-89989 |
| institution | Universiti Teknologi Malaysia - ePrints |
| last_indexed | 2024-03-05T20:49:25Z |
| publishDate | 2020 |
| publisher | SAGE Publications Ltd |
| record_format | dspace |
| spelling | utm.eprints-899892021-03-31T05:03:47Z http://eprints.utm.my/89989/ Payload swing control of a tower crane using a neural network–based input shaper Fasih, S. M. Mohamed, Zaharuddin Husain, Abdul Rashid Ramli, Liyana Abdullahi, Auwalu Anjum, Waqas TK Electrical engineering. Electronics Nuclear engineering This paper proposes an input shaping technique for efficient payload swing control of a tower crane with cable length variations. Artificial neural network is utilized to design a zero vibration derivative shaper that can be updated according to different cable lengths as the natural frequency and damping ratio of the system changes. Unlike the conventional input shapers that are designed based on a fixed frequency, the proposed technique can predict and update the optimal shaper parameters according to the new cable length and natural frequency. Performance of the proposed technique is evaluated by conducting experiments on a laboratory tower crane with cable length variations and under simultaneous tangential and radial crane motions. The shaper is shown to be robust and provides low payload oscillation with up to 40% variations in the natural frequency. With a 40% decrease in the natural frequency, the superiority of the artificial neural network–zero vibration derivative shaper is confirmed by achieving at least a 50% reduction in the overall and residual payload oscillations when compared to the robust zero vibration derivative and extra insensitive shapers designed based on the average operating frequency. It is envisaged that the proposed shaper can be further utilized for control of tower cranes with more parameter uncertainties. SAGE Publications Ltd 2020-08 Article PeerReviewed Fasih, S. M. and Mohamed, Zaharuddin and Husain, Abdul Rashid and Ramli, Liyana and Abdullahi, Auwalu and Anjum, Waqas (2020) Payload swing control of a tower crane using a neural network–based input shaper. Measurement and Control (United Kingdom), 53 (7-8). pp. 1171-1182. ISSN 0020-2940 http://dx.doi.org/10.1177/0020294020920895 |
| spellingShingle | TK Electrical engineering. Electronics Nuclear engineering Fasih, S. M. Mohamed, Zaharuddin Husain, Abdul Rashid Ramli, Liyana Abdullahi, Auwalu Anjum, Waqas Payload swing control of a tower crane using a neural network–based input shaper |
| title | Payload swing control of a tower crane using a neural network–based input shaper |
| title_full | Payload swing control of a tower crane using a neural network–based input shaper |
| title_fullStr | Payload swing control of a tower crane using a neural network–based input shaper |
| title_full_unstemmed | Payload swing control of a tower crane using a neural network–based input shaper |
| title_short | Payload swing control of a tower crane using a neural network–based input shaper |
| title_sort | payload swing control of a tower crane using a neural network based input shaper |
| topic | TK Electrical engineering. Electronics Nuclear engineering |
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