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Direct Adaptive Inverse Control Based on Nonlinear Volterra Model via Fractional LMS Algorithm

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Modeling, Simulation and Optimization

Part of the book series: Smart Innovation, Systems and Technologies ((SIST,volume 292))

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Abstract

This work addresses to perform the direct adaptive inverse control (DAIC) design via fractional least mean square (FLMS) algorithm. Since the controller of DAIC aims to track the plant inverse dynamics as a function of the plant model inverse identification, to track nonlinear dynamics of polynomial type, in this work, the controller is based on Volterra model. Since the performance of an estimation algorithm is important to update the estimate of the controller weight vector, the main objective of this work is to perform the performance analysis of FLMS algorithm, with respect to convergence speed and mean square error (MSE). The proposed analysis was performed on a model containing a polynomial type nonlinearity, represented by a Nonlinear AutoRegressive with eXogenous inputs (NARX) model. In addition, the proposed analysis was performed in the presence of a sinusoidal disturbance signal and time-varying reference signal.

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Authors and Affiliations

  1. Federal University of Maranhão, Graduate Program in Electrical Engineering, São Luĩs, 65080-805, Brazil

    Rodrigo Possidônio Noronha

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  1. Rodrigo Possidônio Noronha
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Correspondence to Rodrigo Possidônio Noronha .

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Editors and Affiliations

  1. Department of Mechanical Engineering, National Institute of Technology Silchar, Silchar, India

    Biplab Das

  2. Department of Computer Science and Engineering, National Institute of Technology Silchar, Silchar, India

    Ripon Patgiri

  3. National Institute of Technology Silchar, Silchar, India

    Sivaji Bandyopadhyay

  4. Department of Automatics and Applied Software, “Aurel Vlaicu” University of Arad, Arad, Romania

    Valentina Emilia Balas

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Noronha, R.P. (2022). Direct Adaptive Inverse Control Based on Nonlinear Volterra Model via Fractional LMS Algorithm. In: Das, B., Patgiri, R., Bandyopadhyay, S., Balas, V.E. (eds) Modeling, Simulation and Optimization. Smart Innovation, Systems and Technologies, vol 292. Springer, Singapore. https://doi.org/10.1007/978-981-19-0836-1_36

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