Investigation of Controller Parameters Effects for a Flexible Manipulator

Öz

The main objective of this study is to examine the effects of the parameters in the control torque expression of the flexible manipulator. First, the flexible manipulator is modelled based on Assumed Mode Method. Then, the control torque expression is obtained depending on the system energy. In the control performed for the flexible manipulator, the aim is determined as achieving the position objective of the flexible manipulator and damping the oscillations during the movement. For this purpose, Artificial Bee Colony (ABC) Algorithm is performed to determine the parameters in the torque expression. The simulations performed in MATLAB are compared with a study in the literature using the related torque expression. Finally, the simulations are repeated for all coefficient parameters in the torque expression and the necessity of including the relevant parameters in the optimization was examined.

Anahtar Kelimeler

• flexible manipulator
• optimisation
• artificial bee colony
• control

Esnek Uzuvlu Bir Manipülatörde Yapay Arı Kolonisi Algoritması ile Optimize Edilen Kontrolcü Parametreleri Etkilerinin İncelenmesi

Öz

Bu çalışmanın temel amacı, esnek uzuvlu manipülatörün kontrol torku ifadesinde yer alan parametrelerin etkilerinin incelenmesidir. Çalışmada ilk olarak, esnek uzuv varsayılan modlar metodu ile modellenmiştir. Ardından, kontrol torku ifadesi sistem enerjisine bağlı olarak elde edilmiştir. Esnek manipülatör için gerçekleştirilen kontrolde amaç, uzvun istenen konuma ulaşması ve hareket sırasındaki salınımların sönümlenmesi olarak belirlenmiştir. Bu amaç doğrultusunda, tork ifadesinde yer alan katsayı parametrelerinin belirlenmesinde Yapay Arı Kolonisi (ABC) Algoritması kullanılmıştır. MATLAB ortamında gerçekleştirilen simülasyonlar literatürde ilgili tork ifadesini kullanan bir çalışma ile karşılaştırılmıştır. Son olarak tork ifadesinde yer alan tüm katsayı parametreleri için simülasyonlar tekrarlanarak ilgili parametrelerin optimizasyona dahil edilme gerekliliği incelenmiştir.

Anahtar Kelimeler

• Esnek Manipülatör
• optimizasyon
• yapay arı kolonisi
• kontrol

Kaynakça

1. Alam, M. S., & Tokhi, M. O. (2007). Dynamic modelling of a single-link flexible manipulator system: A particle swarm optimisation approach. Journal of Low Frequency Noise Vibration and Active Control, 26(1), 57–72. https://doi.org/10.1260/026309207781487466
2. Dwivedy, S. K., & Eberhard, P. (2006). Dynamic analysis of flexible manipulators, a literature review. Mechanism and Machine Theory, 41(7), 749–777. https://doi.org/10.1016/j.mechmachtheory.2006.01.014
3. Eser, S., & Çetin, S. T. (2021). Optimum control of a flexible single link manipulator with Artificial Bee Colony Algorithm. Proceedings of the Institution of Mechanical Engineers, Part C: Journal of Mechanical Engineering Science. https://doi.org/10.1177/09544062211045480
4. He, W., & Ge, S. S. (2015). Vibration control of a flexible beam with output constraint. IEEE Transactions on Industrial Electronics, 62(8), 5023–5030. https://doi.org/10.1109/TIE.2015.2400427
5. He, W., & Sun, C. (2016). Boundary feedback stabilisation of a flexible robotic manipulator with constraint. International Journal of Control, 89(3), 635–651. https://doi.org/10.1080/00207179.2015.1088966
6. Karaboğa, D. (2005). An Idea Based on Honey Bee Swarm for Numerical Optimisation. TECHNICAL REPORT-TR06.
7. Liu, Z., Liu, J., & He, W. (2016). Adaptive boundary control of a flexible manipulator with input saturation. International Journal of Control, 89(6), 1191–1202. https://doi.org/10.1080/00207179.2015.1125022
8. Liu, Z., Liu, J., & He, W. (2018). Boundary control of an Euler–Bernoulli beam with input and output restrictions. Nonlinear Dynamics, 92(2), 531–541. https://doi.org/10.1007/s11071-018-4073-9

9. Loudini, M. (2013). Modelling and intelligent control of an elastic link robot manipulator. International Journal of Advanced Robotic Systems, 10. https://doi.org/10.5772/51102
10. Meng, Q. X., Lai, X. Z., Wang, Y. W., & Wu, M. (2018). A fast stable control strategy based on system energy for a planar single-link flexible manipulator. Nonlinear Dynamics, 94(1), 615–626. https://doi.org/10.1007/s11071-018-4380-1
11. Sakawa, Y., Matsuno, F., & Fukushima, S. (1985). Modeling and feedback control of a flexible arm. Journal of Robotic Systems, 2(4), 453–472. https://doi.org/10.1002/rob.4620020409
12. Sun, C., Gao, H., He, W., & Yu, Y. (2018). Fuzzy neural network control of a flexible robotic manipulator using assumed mode method. IEEE Transactions on Neural Networks and Learning Systems, 29(11), 5214–5227. https://doi.org/10.1109/TNNLS.2017.2743103
13. Sun, C., He, W., & Hong, J. (2017). Neural Network Control of a Flexible Robotic Manipulator Using the Lumped Spring-Mass Model. IEEE Transactions on Systems, Man, and Cybernetics: Systems, 47(8), 1863–1874. https://doi.org/10.1109/TSMC.2016.2562506
14. Supriyono, H., & Tokhi, M. O. (2012). Parametric modelling approach using bacterial foraging algorithms for modelling of flexible manipulator systems. Engineering Applications of Artificial Intelligence, 25(5), 898–916. https://doi.org/10.1016/j.engappai.2012.03.004
15. Xu, B. (2018). Composite learning control of flexible-link manipulator using NN and DOB. IEEE Transactions on Systems, Man, and Cybernetics: Systems, 48(11), 1979–1985. https://doi.org/10.1109/TSMC.2017.2700433
16. Yang, H. J., & Tan, M. (2018). Sliding Mode Control for Flexible-link Manipulators Based on Adaptive Neural Networks. International Journal of Automation and Computing, 15(2), 239–248. https://doi.org/10.1007/s11633-018-1122-2
17. Yang, H., & Liu, J. (2016). Distributed piezoelectric vibration control for a flexible-link manipulator based on an observer in the form of partial differential equations. Journal of Sound and Vibration, 363, 77–96. https://doi.org/10.1016/j.jsv.2015.11.001
18. Zhao, Z., He, X., & Ahn, C. K. (2019). Boundary Disturbance Observer-Based Control of a Vibrating Single-Link Flexible Manipulator. IEEE Transactions on Systems, Man, and Cybernetics: Systems, 51(4), 2382–2390. https://doi.org/10.1109/tsmc.2019.2912900