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Self-adaptive grey wolf optimization based adaptive fuzzy aided sliding mode control for robotic manipulator

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Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
As the robotic manipulators are highly nonlinear, it is a challenging task to design, in particular, the PUMA 560 robotic arm with acceptable performance. This paper intends to show the design and development of an adaptive sliding mode controller (SMC) for a robotic manipulator. Since it is not realistic to match the SMC operations with the system model at every time instant, this paper adopts fuzzy inference to replace the system model. This approach successfully achieves the objectives of the experiment, carried out in two stages. In the first stage, it acquires the precise characteristics of the system model for the diverse samples and adequately represents the robotic manipulator. Subsequently, we derive the acquired characteristics in the form of fuzzy rules. In the second stage, we represent the derived fuzzy rules on the basis on adaptive fuzzy membership functions. Further, the approach introduces the self-adaptiveness into a recent algorithm called Grey Wolf Optimization (GWO) in order to establish the adaptive fuzzy membership functions. We then compare the effectiveness of the proposed method with the identified experimental model and the known methods, like SMC, Fuzzy SMC (FSMC), and GWO-SMC. Finally, the comparison with the known methods establishes the effectiveness of the proposed SAGWO-FSMC technique.
Rocznik
Strony
383--409
Opis fizyczny
Bibliogr. 36 poz., rys.
Twórcy
  • Walchand Institute of Technology Electronics Engineering Department, Solapur, Maharashtra, India
autor
  • Walchand Institute of Technology Electronics Engineering Department, Solapur, Maharashtra, India
Bibliografia
  • [1] Alavandar, S. and Nigam, M.J. (2008) Fuzzy PD+I control of a six DOF robot manipulator. Industrial Robot: An International Journal, 35(2): 125–132.
  • [2] Armstrong, B., Khatib, O. and Burdick, J. (1986) The explicit dynamic model and inertial parameters of the PUMA 560 arm. Proceedings. 1986 IEEE International Conference on Robotics and Automation, 510-518.
  • [3] Corradini, M.L., Fossi, V., Giantomassi, A., Ippoliti, G., Longhi, S. and Orlando, G. (2012)Minimal Resource Allocating Networks for Discrete Time Sliding Mode Control of Robotic Manipulators. IEEE Trans- actions on Industrial Informatics, 8(4): 733-745.
  • [4] Efe, M.O. (2008) Fractional Fuzzy Adaptive Sliding-Mode Control of a 2- DOF Direct-Drive Robot Arm. IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics), 38(6): 1561-1570.
  • [5] Ekemezie, P.N., Osuagwu, C.C. (2001) A Self-Organising Fuzzy Logic Controller. Nigerian Journal of Technology, 20(1).
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  • [12] Kim, Y.K. and Gibson, J.S. (1991) A variable-order adaptive controller for a manipulator with a sliding flexible link. IEEE Transactions on Robotics and Automation, 7(6): 818-827.
  • [13] Kuo, T.C., Huang, Y.J. and Hong, B.W. (2011) Design of Adaptive Sliding Mode Controller for Robotic Manipulators Tracking Control. International Journal of Computer, Electrical, Automation, Control and Information Engineering, 5(5): 453-457.
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  • [16] Li, Y., Ling, L. and Chen, J. (2015) Combined grey prediction fuzzy control law with application to road tunel ventilation system. Journal of Applied Research and Technology, 13(2): 313-320.
  • [17] Lian, R. (2012) Design of an enhanced adaptive self-organizing fuzzy slidingmode controller for robotic systems. Expert Systems with Applications, 39(1): 1545-1554.
  • [18] Lian, R. (2013) Enhanced adaptive grey-prediction self-organizing fuzzy slidingmode controller for robotic systems. Information Sciences, 236, 186-204.
  • [19] Lian, R.J. (2014) Adaptive Self-Organizing Fuzzy Sliding-Mode Radial Basis- Function Neural-Network Controller for Robotic Systems. IEEE Transactions on Industrial Electronics, 61(3): 1493-1503.
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Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-e49fd5f4-df6b-4665-8f87-22865c901333
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