Location via proxy:   [ UP ]  
[Report a bug]   [Manage cookies]                

A novel Robust Adaptive Control Using RFWNNs and Backstepping for Industrial Robot Manipulators with Dead-Zone

https://doi.org/10.1007/s10846-019-01089-9

Видання: Journal of Intelligent & Robotic Systems, 2019, № 3-4, с. 679-692

Видавець: Springer Science and Business Media LLC

Автори: Nguyen Xuan Quynh, Wang Yao Nan, Vu Thi Yen

Список літератури

  1. Wang, L.X.: Stable Adaptive Fuzzy Control of Nonlinear Systems. IEEE Trans. Fuzzy Syst. 1(2), 46–155 (1993)
    https://doi.org/10.1109/91.227383
  2. Tong, S., Chen, B., Wang, Y.: Fuzzy adaptive output feedback control for MIMO nonlinear systems. Fuzzy Sets Syst. 156, 285–299 (2005)
    https://doi.org/10.1016/j.fss.2005.06.011
  3. Tong, S., Li, H.X.: Fuzzy Adaptive Sliding – Mode Control for MIMO Nonlinear Systems. IEEE Trans. Fuzzy Syst. 11(3), 354–360 (2003)
    https://doi.org/10.1109/TFUZZ.2003.812694
  4. Pan, W., Lyu, M., Hwang, K.S., Ju, M.Y., Shi, H.B.: A neuro Fuzzy Visual Servoing Controller for an Articulated Manipulator. IEEE Access. 6, 3346–3357 (2018)
    https://doi.org/10.1109/ACCESS.2017.2787738
  5. Yesim, O., Okyay, K.: Control of a direct drive robot using fuzzy spiking neural networks with variable structure systems-based learning algorithm. Neurocomputing. 149, 690–699 (2015)
    https://doi.org/10.1016/j.neucom.2014.07.061
  6. Sabahi, K., Ghaemi, S., Liu, J., Badamchizadeh, M.A.: Indirect predictive type-2 fuzzy neural network controller for a class of nonlinear input - delay systems. ISA Trans. 71, 185–195 (2017)
    https://doi.org/10.1016/j.isatra.2017.09.009
  7. Wai, R.J., Chen, P.C.: Robust Neural-Fuzzy-Network Control for Robot Manipulator Including Actuator Dynamics. IEEE Trans. Ind. Electron. 53(4), 1328–1349 (2006)
    https://doi.org/10.1109/TIE.2006.878297
  8. Wai, R.J., Lin, Y.W.: Adaptive Moving-Target Tracking Control of a Vision-Based Mobile Robot via a Dynamic Petri Recurrent Fuzzy Neural Network. IEEE Trans. Fuzzy Syst. 21(4), 688–701 (2013)
    https://doi.org/10.1109/TFUZZ.2012.2227974
  9. Wang, Y.C., Chien, C.J., Teng, C.C.: Direct Adaptive Iterative Learning Control of Nonlinear Systems Using an Output-Recurrent Fuzzy Neural Network. IEEE Transaction on systems, Man, and cybernetics – part b: Cybernetics. 34(3), 1348–1359 (2004)
    https://doi.org/10.1109/TSMCB.2004.824525
  10. Lin, F.J., Shieh, P.H.: Recurrent RBFN-Based Fuzzy Neural Network Control for X-Y-Θ Motion Control Stage Using Linear Ultrasonic Motors. IEEE Trans. Ultrason. Ferroelectr. Freq. Control. 53(12), 2450–2464 (2006)
    https://doi.org/10.1109/TUFFC.2006.193
  11. Lin, F.J., Sun, I.F., Yang, K.J., Chang, J.K.: Recurrent Fuzzy Neural Cerebellar Model Articulation Network Fault-Tolerant Control of Six-Phase Permanent Magnet Synchronous Motor Position Servo Drive. IEEE Trans. Fuzzy Syst. 24(1), 153–167 (2016)
    https://doi.org/10.1109/TFUZZ.2015.2446535
  12. Lin, F.J., Huang, P.K., Chou, W.D.: Recurrent-Fuzzy-Neural-Network-Controlled Linear Induction Motor Servo Drive Using Genetic Algorithms. IEEE Trans. Ind. Electron. 54(3), 1449–1461 (2007)
    https://doi.org/10.1109/TIE.2007.892256
  13. Dehghan, S.A.M., Danesh, M., Sheikholeslam, F., Zekri, M.: Adaptive force–environment estimator for manipulators based on adaptive wavelet neural network. Appl. Soft Comput. 28, 527–540 (2015)
    https://doi.org/10.1016/j.asoc.2014.12.021
  14. Wei, S., Wang, Y., Zuo, Y.: Wavelet neural networks robust control of farm transmission line deicing robot manipulators. Computer Standards & Interfaces. 34, 327–333 (2012)
    https://doi.org/10.1016/j.csi.2011.11.001
  15. Khan, M.M., Mendes, A., Zhang, P., Chalup, S.K.: Evolving multi-dimensional wavelet neural networks for classification using Cartesian Genetic Programming. Neurocomputing. 247, 39–58 (2017)
    https://doi.org/10.1016/j.neucom.2017.03.048
  16. Fayez, F.M.E.S., Khaled, A.A.: Adaptive Nonlinear Disturbance Observer Using a Double-Loop Self-Organizing Recurrent Wavelet Neural Network for a Two-Axis Motion Control System. IEEE Trans. Ind. Appl. 54(1), 764–786 (2018)
    https://doi.org/10.1109/TIA.2017.2763584
  17. Lin, F.J., Hung, Y.C., Ruan, K.C.: An Intelligent Second-Order Sliding-Mode Control for an Electric Power Steering System Using a Wavelet Fuzzy Neural Network. IEEE Trans. Fuzzy Syst. 22(6), 1598–1611 (2014)
    https://doi.org/10.1109/TFUZZ.2014.2300168
  18. Wu, X., Wang, Y.N., Dang, X.J.: Robust adaptive sliding-mode control of condenser-cleaning mobile manipulator using fuzzy wavelet neural network. Fuzzy Sets Syst. 235, 62–82 (2014)
    https://doi.org/10.1016/j.fss.2013.07.009
  19. Rahib, H.A., Okyay, K.: Fuzzy Wavelet Neural Networks for Identification and Control of Dynamic Plants—A Novel Structure and a Comparative Study. IEEE Trans. Ind. Electron. 55(8), 3133–3140 (2008)
    https://doi.org/10.1109/TIE.2008.924018
  20. Tsai, C.H., Chuang, H.T.: Deadzone compensation based on constrained RBF neural network. Journal of the Franklin Institute. 341, 361–374 (2004)
    https://doi.org/10.1016/j.jfranklin.2004.01.001
  21. He, W., Dong, Y., Sun, C.: Adaptive neural network control of unknown nonlinear affine systems with input deadzone and output constraint. ISA Trans. 58, 96–104 (2014)
    https://doi.org/10.1016/j.isatra.2015.05.014
  22. Han, S.I., Lee, J.: Finite-time sliding surface constrained control for a robot manipulator with an unknown deadzone and disturbance. ISA Trans. 65, 307–318 (2016)
    https://doi.org/10.1016/j.isatra.2016.07.013
  23. Selmic, R., Lewis, F.L.: Deadzone compensation in motion control systems using neural networks. IEEE Trans. Autom. Control. 45(4), 602–613 (2000)
    https://doi.org/10.1109/9.847098
  24. Abiyev, R.H., Kaynak, O.: Fuzzy Wavelet Neural Networks for Identification and Control of Dynamic Plants—A Novel Structure and a Comparative Study. IEEE Trans. Industrial Electronics. 55(8), 3133–3140 (2008)
    https://doi.org/10.1109/TIE.2008.924018
  25. Lewis, F.L., Tim, K., Wang, L.Z., Li, Z.X.: Deadzone compensation in motion control systems using adaptive fuzzy control system. IEEE Trans. Control Syst. Technol. 7(6), 731–742 (1999)
    https://doi.org/10.1109/87.799674
  26. Slotine, J.J.E., Li, W.: Applied Nonlinear Control. Prentice – Hall, Hoboken (1991)

Публікації, які цитують цю публікацію

Position Tracking Control of Robotic System with Time-varying Delay and Dead-zone

Xia Liu, Shini Chen, Yong Yang

https://doi.org/10.1016/j.ifacol.2022.04.066 ·

2022, IFAC-PapersOnLine, № 1, с. 399-404

Scopus
Цитувань Crossref: 0

Trajectory Tracking of Robotic Arm Based on Power Regulation of Actuator Using Neural Averaged Subgradient Control

A. Hernandez-Sanchez, C. Mireles-Perez, A. Poznyak, O. Andrianova, V. Chertopolokhov, I. Chairez

https://doi.org/10.1016/j.ifacol.2022.07.018 ·

2022, IFAC-PapersOnLine, № 9, с. 99-104

Scopus
Цитувань Crossref: 0

Event-Triggered Adaptive Hybrid Position-Force Control for Robot-Assisted Ultrasonic Examination System

Mohamed Abbas, Sami Al Issa, Santosha K. Dwivedy

https://doi.org/10.1007/s10846-021-01428-9

2021, Journal of Intelligent & Robotic Systems, № 4

Scopus
WoS
Цитувань Crossref: 34

Rules-reduced fuzzy neural network-based learning control for multiple constraints robots using online identification and compensation methods

Du Xu, Bowen Xu, Tete Hu, Lairong Yin

https://doi.org/10.1016/j.ins.2024.121060 ·

2024, Information Sciences, с. 121060

Scopus
WoS
Цитувань Crossref: 0
Знайти всі цитування публікації
Дані публікації
Тип публікації Журнальна стаття
Кількість цитувань 1
Кількість джерел у списку літератури: 26
Видання індексується в Scopus Так
Видання індексується в Web of Science Так
Пошук