Active vibration control of flexible beam incorporating recursive least square and neural network algorithms.
Thesis (PhD. (Mechanical Engineering))
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| Format: | Thesis |
| Language: | English |
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Universiti Teknologi Malaysia
2024
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| Online Access: | https://openscience.utm.my/handle/123456789/1526 |
| _version_ | 1824446320639737856 |
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| author | Abd. Jalil, Nurhanafifi |
| author_facet | Abd. Jalil, Nurhanafifi |
| author_sort | Abd. Jalil, Nurhanafifi |
| collection | OpenScience |
| description | Thesis (PhD. (Mechanical Engineering)) |
| first_indexed | 2025-02-19T01:13:17Z |
| format | Thesis |
| id | oai:openscience.utm.my:123456789/1526 |
| institution | Universiti Teknologi Malaysia - OpenScience |
| language | English |
| last_indexed | 2025-02-19T01:13:17Z |
| publishDate | 2024 |
| publisher | Universiti Teknologi Malaysia |
| record_format | dspace |
| spelling | oai:openscience.utm.my:123456789/15262024-12-18T13:00:43Z Active vibration control of flexible beam incorporating recursive least square and neural network algorithms. Abd. Jalil, Nurhanafifi Vibration and shock Vibration isolation, damping Thesis (PhD. (Mechanical Engineering)) In recent years, active vibration control (AVC) has emerged as an important area of scient ific study especially for vibrat ion suppression of flexible structures. Flexible structures offer great advantages in contrast to the conventional structures, but necessary action must be taken for cancelling the unwanted vibration. In this research, a simulation algorithm represent ing flexible beam with specific condit ions was derived from Euler Bernoulli beam theory. The proposed finite difference (FD) algorithm was developed in such way that it allows the disturbance excitat ion at various points. The predicted resonance frequencies were recorded and validated with theoretical and experimental values. Subsequent ly, flexible beam test rig was developed for collecting data to be used in system ident ificat ion (SI) and controller development. The experimental rig was also utilised for implementation and validat ion of controllers. In this research, parametric and nonparametric SI approaches were used for characterising the dynamic behaviour of a lightweight flexible beam using input - output data collected experimentally. Tradit ional recursive least square (RLS) method and several artificial neural network (ANN) architectures were utilised in emulat ing this highly nonlinear dynamic system here. Once the model of the system was obtained, it was validated through a number of validation tests and compared in terms of their performance in represent ing a real beam. Next, the development of several convent ional and intelligent control schemes with collocated and non-collocated actuator sensor configurat ion for flexible beam vibrat ion attenuation was carried out. The invest igat ion involves design of convent ional proportional-integral-derivat ive (PID) based, Inverse recursive least square active vibrat ion control (RLS-AVC), Inverse neuro active vibration control (Neuro-AVC), Inverse RLS-AVC with gain and Inverse Neuro-AVC with gain controllers. All the developed controllers were tested, verified and validated experimentally. A comprehensive comparat ive performance to highlight the advantages and drawbacks of each technique was invest igated analyt ically and experimentally. Experimental results obtained revealed the superiorit y of Inverse RLS-AVC with gain controller over convent ional method in reducing the crucial modes of vibration of flexible beam structure. Vibration attenuation achieved using proportional (P), proportional-integral (PI), Inverse RLS-AVC, Inverse Neuro- AVC, Inverse RLS-AVC with gain and Inverse Neuro-AVC with gain control strategies are 9.840 dB, 6.840 dB, 9.380 dB, 8.590 dB, 17.240 dB and 5.770 dB, respectively. Faculty of Mechanical Engineering 2024-12-18T04:36:28Z 2024-12-18T04:36:28Z 2017 Thesis Dataset https://openscience.utm.my/handle/123456789/1526 en application/pdf application/pdf application/pdf application/pdf Universiti Teknologi Malaysia |
| spellingShingle | Vibration and shock Vibration isolation, damping Abd. Jalil, Nurhanafifi Active vibration control of flexible beam incorporating recursive least square and neural network algorithms. |
| title | Active vibration control of flexible beam incorporating recursive least square and neural network algorithms. |
| title_full | Active vibration control of flexible beam incorporating recursive least square and neural network algorithms. |
| title_fullStr | Active vibration control of flexible beam incorporating recursive least square and neural network algorithms. |
| title_full_unstemmed | Active vibration control of flexible beam incorporating recursive least square and neural network algorithms. |
| title_short | Active vibration control of flexible beam incorporating recursive least square and neural network algorithms. |
| title_sort | active vibration control of flexible beam incorporating recursive least square and neural network algorithms |
| topic | Vibration and shock Vibration isolation, damping |
| url | https://openscience.utm.my/handle/123456789/1526 |
| work_keys_str_mv | AT abdjalilnurhanafifi activevibrationcontrolofflexiblebeamincorporatingrecursiveleastsquareandneuralnetworkalgorithms |