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Neural Network Identification Based Multivariable Feedback Linearization Robust Control for a Two-Link Manipulator

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Abstract

Regarding to the variations of the load and unmodeled dynamic, robot manipulators are known as a nonlinear dynamic system. Overcoming such problems like uncertainties and nonlinear characteristics in the model of two-link manipulator is the principal goal of this paper. To approach to this aim, a neural network is combined with a linear robust control in which the result has the advantages of, the first, approximated nonlinear elements and the second, the guaranteed robustness. To design the proposed controller, at first, multivariable feedback linearization is employed to convert the nonlinear model to linear one. Second, the unknown parameters of the system are identified by neural network based on a new proposed learning rule. Third, Mixed linear feedback-H ∞  robust control method is proposed to stabilize the closed loop system. The closed loop system based on the proposed controller is analyzed and some numerical simulations are performed. Results show suitable responses of the closed loop system.

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Authors and Affiliations

  1. Department of Engineering, Chalos Branch, Islamic Azad University, Chalos, Iran

    Morteza Moradi

  2. Imam Khomeini Maritime Sciences University, Nowshahr, Mazandaran, Iran

    Hamid Malekizade

Authors
  1. Morteza Moradi
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  2. Hamid Malekizade
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Correspondence to Morteza Moradi.

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Moradi, M., Malekizade, H. Neural Network Identification Based Multivariable Feedback Linearization Robust Control for a Two-Link Manipulator. J Intell Robot Syst 72, 167–178 (2013). https://doi.org/10.1007/s10846-013-9827-5

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  • DOI: https://doi.org/10.1007/s10846-013-9827-5

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