https://doi.org/10.4081/jae.2023.1466

Fuzzy neural network PID control design of camellia fruit vibration picking manipulator

Vol. 54 No. 2 (2023)
    Published: 1 August 2023
    Camellia fruit picking machine, vibration frequency PID control, fuzzy wavelet neural network
    Abstract Views: 762
    PDF: 245
    Appendix: 45
    HTML: 4
    Publisher's note
    All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.

    Authors

    Ziyan Fan
    956931430@qq.com
    https://orcid.org/0000-0002-4781-666X
    College of Mechanical and Electrical Engineering, Central South University of Forestry and Technology, Changsha, China.
    Lijun Li
    College of Mechanical and Electrical Engineering, Central South University of Forestry and Technology, Changsha, China.
    Zicheng Gao
    College of Mechanical and Electrical Engineering, Central South University of Forestry and Technology, Changsha, China.

    Due to the growth characteristics of the flowers and fruits of camellia in the same period, the vibrating camellia fruit picking machine needs to ensure the constant rotational speed of the vibrating hydraulic motor when the picking mechanism is operating, to achieve a constant vibration frequency, to ensure that the camellia fruit can smoothly fall off the branches through vibration. In contrast, the camellia fruit does not fall off. In this regard, this paper deduced the state space equation of the camellia fruit picking machine’s valve-controlled vibrating hydraulic motor system and designed a fuzzy wavelet neural network PID controller (FWNN PID controller) based on the traditional incremental PID control principle. Then the designed vibration picking manipulator control system was simulated under no-load, 5 s load conditions, and load start conditions with MATLAB/Simulink, a general PID controller and a fuzzy RBF neural network PID controller (FRBFNN PID controller) were used to contrast with it. The results show that the general PID controller has a slow response speed and poor robustness, while fuzzy neural network PID controllers (including FWNN PID controller and FRBFNN PID controller) have a fast response speed and strong robustness, which can well meet the requirements of a specific vibration frequency. Finally, a field test was carried out. The results show that the FWNN PID control is better than the FRBFNN PID control. Furthermore, the FWNN PID controller obviously reduced the drop rate of camellia flowers within 6% while ensuring the picking efficiency above 90%, which can well meet the needs of the camellia fruit picking operation.

    Dimensions

    Altmetric

    PlumX Metrics

    Downloads

    Download data is not yet available.

    Citations

    Crossref
    Scopus
    Google Scholar
    Europe PMC
    Ayas M.S., Altas I.H. 2018. Designing and implementing a plug-in type repetitive controller for a redundantly actuated ankle rehabilitation robot. Proceedings of the Institution of Mechanical Engineers, Part I. J. Syst. Control Engine. 232:592-607. DOI: https://doi.org/10.1177/0959651818762062
    Ayten K.K. 2019. A. Implementation a fractional-order adaptive model-based PID-type sliding mode speed control for wheeled mobile robot. Proceedings of the Institution of Mechanical Engineers, Part I. J. Syst. Control Engine. 233:1067-84. DOI: https://doi.org/10.1177/0959651819847395
    Gao Z.C., Li L.J., Li X. 2013. Development and experiment of the picking actuator of the comb-type camellia fruit picking machine. Trans. Chinese Soc. Agric. Engine. 29:19-25.
    Gao Z.C., Li L.J., Wang P.H., Zhu Q., Luo Y.K. 2016. The design and analysis of Camellia fruit picking ’robots’ picking monomers. J. Central South Univ. Forestry Technol. 36:114-8.
    Gao Z.C., Zhao K.J., Li L.J., Pang G.Y., Wang X.C. 2019. Design and test of suspension vibrating camellia picking executing mechanism. Trans. Chinese Soc. Agric. Engine. 35:9-17.
    Gong C., Yang S.Q. 2019. Research of oil pump control based on fuzzy neural network PID algorithm. Int. J. Adv. Netw. Monit. Control. 3:63-8. DOI: https://doi.org/10.21307/ijanmc-2019-022
    Guo H.L., Li K.W. 2012. Research on control system for sluice gate flow based on fuzzy neural network PID. Appl. Mechan. Mater. 1945:1779-82. DOI: https://doi.org/10.4028/www.scientific.net/AMM.198-199.1779
    Han S.Y., Dong J.F., Zhou J., Chen Y.H. 2022. Adaptive fuzzy PID control strategy for vehicle active suspension based on road evaluation. Electronics. 11:921. DOI: https://doi.org/10.3390/electronics11060921
    Huang D., Rao H.H. 2019. Research present situation of mechanized picking equipment for camellia in China. Forest. Machine. Woodwork. Equip. 47:11-3.
    Karaboga D., Akay B. 2010. Proportional-integral-derivative controller design by using artificial bee colony, harmony search, and the bees algorithms. Proceedings of the Institution of Mechanical Engineers, Part I. J. Syst. Control Engine. 224:869-83. DOI: https://doi.org/10.1243/09596518JSCE954
    Kong L., Yuan J. 2020. New relaxed stabilization conditions for discrete-time Takagi-Sugeno fuzzy control systems. Asian J. Control. 22:1604-16. DOI: https://doi.org/10.1002/asjc.2047
    Lin B., Su X.Y., Li X.H. 2019. Fuzzy sliding mode control for active suspension system with proportional differential sliding mode observer. Asian J. Control. 21:264-76. DOI: https://doi.org/10.1002/asjc.1882
    Meng F.H., Zhao S.S., Yu Z.P., Wang N., Gao F. 2016. Single neural element PID control based on AMESim and Simulink cosimulation for speed governing system of value-controlled motor. Chinese Hydr. Pneumat. 7:83-8.
    Shang X., Zhou H., Yang H.Y. 2019. Research status of active control of hydraulic fluid pulsation. J. Mechan. Engine. 55:216-26. DOI: https://doi.org/10.3901/JME.2019.24.216
    Tsutsumi T., Tanino M., Yamakita J. 2007. Hydraulic valve control method of tractor implements for agricultural applications. J. Japan. Soc. Agric. Machin. 69:83-90.
    Tufan D., Serhat C.M. 2022. Design and robustness analysis of fuzzy PID controller for automatic voltage regulator system using genetic algorithm. Trans. Inst. Measure. Control. 44: 1862-73 DOI: https://doi.org/10.1177/01423312211066758
    Wang C.Y., Li L.J. 2019. Hand-eye calibration algorithm for robot based on quaternion. Transd. Microsyst. Technol. 38:133-5.
    Wang P.H., Li L.J., Gao Z.C., Min S.H., Xiao J. 2015. The design and analysis of oscillating fruit picking head. J. Northw. Forest. Univ. 30:288-91.
    Wang W.J., Chen C.G., Cheng J. 2007. Active role of tea oil in medicine and health care. Food Nutr. China. 9:48-51.
    Wang Z., Huang Y.S. 2019. Robust decentralized adaptive fuzzy control of large-scale nonaffine nonlinear systems with strong interconnection and application to automated highway systems. Asian J. Control. 21:2387-94. DOI: https://doi.org/10.1002/asjc.1834
    Wu Q.C., Zhang Y.M., Chen Y. 2022. Design, control, and experimental verification of a soft knee exoskeleton for rehabilitation during walking. Proceedings of the Institution of Mechanical Engineers, Part I. J. Syst. Control Engine. 236:138-52. DOI: https://doi.org/10.1177/09596518211014325
    Xu J.J., Xiao L., Lin M.Q., Tan X.J., Su J. 2022. Application of fuzzy PID position control algorithm in motion control system design of palletizing robot. Secur. Commun. Netw. 2022. [Epub ahead of print]. DOI: https://doi.org/10.1155/2022/8720960
    Xue F.F., Fan Z.M. 2022. Kinematic control of a cable-driven snake-like manipulator for deep-water based on fuzzy PID controller. Proceedings of the Institution of Mechanical Engineers, Part I. J. Syst. Control Engine. 236:989-98. DOI: https://doi.org/10.1177/09596518211064794
    Yan Z. 2020. Research on dynamic characteristics of angular displacement servo system of valve control hydraulic motor. Chinese Hydr. Pneum. 6:43-51.
    Zhang L.Y., Rao H.H., Huang D.S., Chen B., Liu M.H. 2017. Design and experiment of mechanical picking platform of camellia based on Matlab. China Southern Agric. Machin. 48:22-4.
    Zhang X.Z., Li L.J. 2019. Research of image recognition of camellia oleifera fruit based on improved convolutional autoencoder. J. Forest. Engine. 4:118-24.
    Zhangzhou J.Y., Chen Z.W., Dai J.C., Xu X.Q., Guo Y. 2022. Valve-cylinder integrated modeling and control strategy design for hydraulic servo vibration system. Mechan. Engine. 1:34-8.
    Zhou Z.C., Chen R. 2013. Design on fuzzy neural network PID control system of diesel engine. Adv. Mater. Res. 2534:425-9. DOI: https://doi.org/10.4028/www.scientific.net/AMR.756-759.425

    How to Cite

    Fan, Z., Li, L. and Gao, Z. (2023) “Fuzzy neural network PID control design of camellia fruit vibration picking manipulator”, Journal of Agricultural Engineering, 54(2). doi: 10.4081/jae.2023.1466.
    Copyright (c) 2023 the Author(s) Creative Commons License

    This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.

    PAGEPress has chosen to apply the Creative Commons Attribution NonCommercial 4.0 International License (CC BY-NC 4.0) to all manuscripts to be published.


    Similar Articles

    1 2 3 4 5 6 7 8 9 10 > >> 

    You may also start an advanced similarity search for this article.