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Adaptive uncertainty compensation-based nonlinear model predictive control with real-time applications

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Abstract

In this paper, an adaptive model predictive controller (MPC) with a function approximator is proposed to the control of the uncertain nonlinear systems. The proposed adaptive Sigmoid and Chebyshev neural networks-based MPCs (ANN-MPC and ACN-MPC) compensate the system uncertainty and control the system accurately. Using Lyapunov theory, the closed-loop signals of the linearized dynamics and the uncertainty modeling-based model predictive controller have been proved to be bounded. Accuracy of the ANN-MPC and ACN-MPC has been compared with the Runge–Kutta discretization-based nonlinear MPC on an experimental MIMO three-tank liquid-level system where a functional uncertainty is created on its dynamics. Real-time experimental results demonstrate the effectiveness of the proposed controllers. In addition, due to the faster function approximation capability of Chebyshev polynomial networks, ACN-MPC provided better control performance results.

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Author information

Authors and Affiliations

  1. Department of Computer Engineering, Pamukkale University, 20070, Denizli, Turkey

    Meriç Çetin

  2. Department of Electricity and Energy, Denizli Vocational School, Pamukkale University, 20070, Denizli, Turkey

    Bedri Bahtiyar

  3. Department of Electrical and Electronics Engineering, Pamukkale University, 20070, Denizli, Turkey

    Selami Beyhan

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  1. Meriç Çetin
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  2. Bedri Bahtiyar
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  3. Selami Beyhan
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Correspondence to Meriç Çetin.

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We have received no support or commercial funding for this paper; therefore, we have no conflicts of interest to declare.

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Çetin, M., Bahtiyar, B. & Beyhan, S. Adaptive uncertainty compensation-based nonlinear model predictive control with real-time applications. Neural Comput & Applic 31 (Suppl 2), 1029–1043 (2019). https://doi.org/10.1007/s00521-017-3068-7

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  • DOI: https://doi.org/10.1007/s00521-017-3068-7

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