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One DOF robot manipulator control through type-2 fuzzy robust adaptive controller

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Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
In this article, the one DOF robot manipulator control is assessed through second type robust fuzzy-adaptive controller. The objective is to obtain a tracking path with appropriate accuracy. The stability of the closed loop system is verified through Lyapunov stability theory and the efficiency of tracking is analyzed subject to the constraints and uncertainty. In order to design the fuzzy controller a set of if-then fuzzy rules are considered which describe the system input-output behavior. Simulation and the results of the experiments on the one DOF robots indicate the effectiveness of the proposed methods.
Twórcy
  • Department of Electrical Engineering, Najaf Abad Branch, Islamic Azad University, Isfahan, Iran, www: http://researchgate.net/amir_naderolasli/
  • Department of Electrical Engineering, Najaf Abad Branch, Islamic Azad University, Isfahan, Iran, www: http://research.iaun.ac.ir/pd/abbas.chatraei/
Bibliografia
  • [1] A. T. Azar and F. E. Serrano, “Fractional Order Two Degree of Freedom PID Controller for a Robotic Manipulator with a Fuzzy Type-2 Compensator”. In: A. E. Hassanien, M. F. Tolba, K. Shaalan, and A. T. Azar, eds., Proceedings of the International Conference on Advanced Intelligent Systems and Informatics 2018, 2019, 77–88.
  • [2] Byung Kook Yoo and Woon Chul Ham, “Adaptive control of robot manipulator using fuzzy compensator”, IEEE Transactions on Fuzzy Systems, vol. 8, no. 2, 2000, 186–199, 10.1109/91.842152.
  • [3] W. Chang, S. Tong, and Y. Li, “Adaptive fuzzy backstepping output constraint control of flexible manipulator with actuator saturation”, Neural Computing and Applications, vol. 28, no. 1, 2017, 1165–1175, 10.1007/s00521-016-2425-2.
  • [4] M. J. Er and S. Mandal, “A Survey of Adaptive Fuzzy Controllers: Nonlinearities and Classi􀏐ications”, IEEE Transactions on Fuzzy Systems, vol. 24, no. 5, 2016, 1095–1107, 10.1109/TFUZZ.2015.2501439.
  • [5] M. M. Fateh and M. Souzanchikashani, “Indirect adaptive fuzzy control for flexible-joint robot manipulators using voltage control strategy”, Journal of Intelligent & Fuzzy Systems, vol. 28, no. 3, 2015, 1451–1459, 10.3233/IFS-141430.
  • [6] J. He, M. Luo, Q. Zhang, J. Zhao, and L. Xu, “Adaptive Fuzzy Sliding Mode Controller with Nonlinear Observer for Redundant Manipulators Handling Varying External Force”, Journal of Bionic Engineering, vol. 13, no. 4, 2016, 600–611, 10.1016/S1672-6529(16)60331-1.
  • [7] W. He, A. O. David, Z. Yin, and C. Sun, “Neural Network Control of a Robotic Manipulator With Input Deadzone and Output Constraint”, IEEE Transactions on Systems, Man, and Cybernetics: Systems, vol. 46, no. 6, 2016, 759–770, 10.1109/TSMC.2015.2466194.
  • [8] W. He, Y. Dong, and C. Sun, “Adaptive Neural Impedance Control of a Robotic Manipulator with Input Saturation”, IEEE Transactions on Systems, Man, and Cybernetics: Systems, vol. 46, no. 3,2016, 334–344, 10.1109/TSMC.2015.2429555.
  • [9] X. Huang, H. Gao, J. Li, R. Mao, and J. Wen, “Adaptive back-stepping tracking control of robot manipulators considering actuator dynamic”. In: 2016 IEEE International Conference on Advanced Intelligent Mechatronics (AIM), 2016, 941–946, 10.1109/AIM.2016.7576890.
  • [10] B. Kharabian, H. Bolandi, S. M. Smailzadeh, and S. K. Mousavi Mashhadi, “Fuzzy switching for multiple model adaptive control in manipulator robot”, Journal of Automation, Mobile Robotics and Intelligent Systems, vol. Vol. 11, No. 1, 2017, 10.14313/JAMRIS_1-2017/7.
  • [11] C.-H. Lee and W.-C. Wang, “Robust adaptive position and force controller design of robot manipulator using fuzzy neural networks”, Nonlinear Dynamics, vol. 85, no. 1, 2016, 343–354, 10.1007/s11071-016-2689-1.
  • [12] M. Li, Y. Li, S. S. Ge, and T. H. Lee, “Adaptive Control of Robotic Manipulators With Unified Motion Constraints”, IEEE Transactions on Systems, Man,and Cybernetics: Systems, vol. 47, no. 1, 2017,184–194, 10.1109/TSMC.2016.2608969.
  • [13] Y. Li, S. Tong, and T. Li, “Hybrid Fuzzy Adaptive Output Feedback Control Design for UncertainnMIMO Nonlinear Systems With Time-Varying Delays and Input Saturation”, IEEE Transactions on Fuzzy Systems, vol. 24, no. 4, 2016, 841–853, 10.1109/TFUZZ.2015.2486811.
  • [14] C. Lin, “Nonsingular Terminal Sliding Mode Control of Robot Manipulators Using Fuzzy Wavelet Networks”, IEEE Transactions on Fuzzy Systems, vol. 14, no. 6, 2006, 849–859, 10.1109/TFUZZ.2006.879982.
  • [15] A. Medjebouri and L. Mehennaoui, “Adaptive Neuro-Sliding Mode Control of PUMA 560 Robot Manipulator”, Journal of Automation, Mobile Robotics and Intelligent Systems, vol. Vol. 10, No. 4, 2016, 10.14313/JAMRIS_4-2016/27.
  • [16] A. Naderolasli and M. Tabatabaei, “Stabilization of the Two-Axis Gimbal System Based on an Adaptive Fractional-Order Sliding-Mode Controller”, IETE Journal of Research, vol. 63, no. 1, 2017, 124–133, 10.1080/03772063.2016.1229581.
  • [17] S. R. Naghibi, A. A. Pirmohamadi, and S. A. A. Moosavian, “Fuzzy MTEJ controller with integrator for control of underactuated manipulators”, Robotics and Computer-Integrated Manufacturing, vol. 48, 2017, 93–101, 10.1016/j.rcim.2017.03.006.
  • [18] N. Nikdel, M. A. Badamchizadeh, V. Azimirad, and M. A. Nazari, “Adaptive backstepping control for an n-degree of freedom robotic manipulator based on combined state augmentation”, Robotics and Computer-Integrated Manufacturing, vol. 44, 2017, 129–143, 10.1016/j.rcim.2016.08.007.
  • [19] G. G. Rigatos, “Adaptive fuzzy control of DC motors using state and output feedback”, Electric Power Systems Research, vol. 79, no. 11, 2009, 1579–1592, 10.1016/j.epsr.2009.06.007.
  • [20] C. Sun, H. Gao, W. He, and Y. Yu, “Fuzzy Neural Network Control of a Flexible Robotic Manipulator Using Assumed Mode Method”, IEEE Transactions on Neural Networks and Learning Systems, vol. 29, no. 11, 2018, 5214–5227, 10.1109/TNNLS.2017.2743103.
  • [21] C. Yang, Y. Jiang, W. He, J. Na, Z. Li, and B. Xu, “Adaptive Parameter Estimation and Control Design for Robot Manipulators With Finite-Time Convergence”, IEEE Transactions on Industrial Electronics, vol. 65, no. 10, 2018, 8112–8123, 10.1109/TIE.2018.2803773.
  • [22] S. Zhang, Y. Dong, Y. Ouyang, Z. Yin, and K. Peng, “Adaptive Neural Control for Robotic Manipulators With Output Constraints and Uncertainties”, IEEE Transactions on Neural Networks and Learning Systems, vol. 29, no. 11, 2018, 5554–5564, 10.1109/TNNLS.2018.2803827.
  • [23] Q. Zhou, S. Zhao, H. Li, R. Lu, and C. Wu, “Adaptive Neural Network Tracking Control for Robotic Manipulators With Dead Zone”, IEEE Transactions on Neural Networks and Learning Systems, 2019, 1–10, 10.1109/TNNLS.2018.2869375.
  • [24] M. M. Zirkohi, M. M. Fateh, and M. A. Shoorehdeli,“Type-2 Fuzzy Control for a Flexible- joint Robot Using Voltage Control Strategy”, International Journal of Automation and Computing, vol. 10, no. 3, 2013, 242–255, 10.1007/s11633-013-0717-x.
Uwagi
Opracowanie rekordu w ramach umowy 509/P-DUN/2018 ze środków MNiSW przeznaczonych na działalność upowszechniającą naukę (2019).
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-2d897d6c-9049-40a2-8685-b2a28936899d
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