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ANFIS-based PID continuous sliding mode controller for robot manipulators in joint space

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Języki publikacji
EN
Abstrakty
EN
This paper presents a feasible design for a con- trol algorithm to synthesize an adaptive neuro-fuzzy inference system-based PID continuous sliding mode control system (ANFIS- PIDCSMC) for adaptive trajectory tracking control of the rigid robot manipulators (RRMs) in the joint space. First, a PID sliding mode control algorithm with sliding surface dynamics-based continuous proportional-integral (PI) control action (PIDSMC-SSDCPI) is presented. The global stability conditions are formulated in terms of Lyapunov full quadratic form such that the robot system output can track the desired reference output. Second, to increase the control system robustness, the PI control action in the PIDSMC- SSDCPI controller is supplanted by an ANFIS control signal to provide a control approach that can be termed adaptive neuro-fuzzy inference system-based PID continuous sliding mode control system (ANFIS-PIDCSMC). For the proposed control algorithm, numerical simulations using the dynamic model of RRM with uncertainties and external disturbances show high quality and effectiveness of the adopted control approach in high-speed trajectory tracking control problems. The simulation results that are compared with the results, obtained for the traditional controllers (standalone PID and traditional sliding mode controller (TSMC)), illustrate the fact that the tracking control behavior of the robot system achieves acceptable tracking performance.
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Rocznik
Strony
525--555
Opis fizyczny
Bibliogr. 40 poz., rys., tab.
Twórcy
  • Department of Computers and Control Engineering, Tanta University, Egypt
  • Higher Institute of Engineering and Technology in Kafr Elsheikh (HIET), Egypt
  • Department of Computers and Control Engineering, Tanta University, Egypt
  • Department of Computers and Control Engineering, Tanta University, Egypt
Bibliografia
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  • Alavandar, S. and Nigam, M. J. (2009a) Comparative Analysis of Conventional and Soft Computing Based Control Strategies for Robot Manipulators with Uncertainties. International Journal of Computational Cognition, 7, 1, 52-61.
  • Alavandar, S. and Nigam, M. J. (2009b) New Hybrid Adaptive Neuro Fuzzy Algorithms for Manipulator Control with Uncertainties – Comparative Study. ISA Trans., 48, 4, 497-502.
  • Alavandar, S. and Nigam, M. (2009c) New hybrid adaptive neuro-fuzzy algorithms for manipulator control with uncertainties - Comparative study. ISA Transactions, 48, 4, 497-502.
  • Aloui, S., Pag`es, O., El Hajjaji, A., Chaari, A. and Koubaa, Y. (2010) Improved fuzzy sliding mode control for a class of MIMO nonlinear uncertain and perturbed systems. Applied Soft Computing, 11, 1, 820-826.
  • Angeles, I. (2003) Fundamentals of Robotic Mechanical Systems, second edition. Springer-Verlag, New York.
  • Barambones, O. and Etxebarria, V. (2000) Robust adaptive control for robot manipulator with un-modeled dynamics. Int. J. Cybernet. Syst., 31, 1, 67–86.
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  • Capisani, L. M., Ferrara A. and Magnani, L. (2007) Second Order Sliding Mode Motion Control of Rigid Robot Manipulators. Proceedings of the 46th IEEE Conference on Decision and Control New Orleans, LA, USA, 12-14.
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  • Gong, J. Q. and Yao, B. (1999) Adaptive robust control without knowing bounds of parameter variations. Proc. IEEE Conf. Decision Control, 4, 3334–3339.
  • Ho, H. F., Wong, Y. K. and Rad, A. B. (2007) Robust fuzzy tracking control for robotic manipulators. Simulat. Model. Pract. Theory, 15, 801–816.
  • Hu, J., Wang, Z., Gao, H. and Stergioulas, L. K. (2012) Robust H∞ sliding mode control for discrete time-delay systems with stochastic non-linearities. Journal of Franklin Institute, 349, 1459-1479.
  • Zheng, E.-H., Xiong, J.-J. and Luo, J.-L. (2014) Second order sliding mode control for a quadrotor UAV. ISA Transactions, 53.
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  • Jang, J. (1993) ANFIS: adaptive-network-based fuzzy inference systems. IEEE Transactions on Systems, Man and Cybernetics, 23, 3, 665–685.
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  • Mahmoodabadi, M. J., Taherkhorsandi, M. and Bagheri, A. (2014) Optimal robust sliding mode tracking control of a biped robot based on ingenious multi-objective PSO. Neurocomputing, 124, 194-209.
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  • Ortega, R. and Spong, M. (1989) Adaptive motion control of rigid robots: A tutorial. Automatica, 25, 6, 877-888.
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  • Sun, T., Pei, H., Pan, Y., Zhou, H. and Zhang, C. (2011) Neural network-based sliding mode adaptive control for robot manipulators. Neurocomputing, 74, 2377-2384.
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  • Wang, J., Zong, Q., Su, R. and Tian, B. (2014) Continuous high order sliding mode controller design for a flexible air-breathing hypersonic vehicle. ISA Transactions, 53, 690–698.
  • Xiang, W. and Chen, F. (2011) An adaptive sliding mode control scheme for a class of chaotic systems with mismatched perturbations and input nonlinearities. Communications in Nonlinear Science and Numerical Simulation, 16, 1-9.
  • Yu, H. and Lloyd, S. (1997) Variable structure adaptive control of robot manipulators. IEEE Proceedings - Control Theory and Applications, 144, 167-176.
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Uwagi
Opracowanie rekordu ze środków MNiSW, umowa Nr 461252 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2021).
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-6bd32d2b-56cc-4eeb-b0a0-14bad083a268
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