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Neural Computing and Applications

, Volume 31, Supplement 2, pp 1029–1043 | Cite as

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

  • Meriç ÇetinEmail author
  • Bedri Bahtiyar
  • Selami Beyhan
Original Article
  • 230 Downloads

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.

Keywords

Model predictive control Adaptive neural network Chebyshev polynomial network Uncertainty compensation Stability Three-tank liquid-level system Real-time control 

Notes

Compliance with ethical standards

Conflict of interest

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

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Copyright information

© The Natural Computing Applications Forum 2017

Authors and Affiliations

  1. 1.Department of Computer EngineeringPamukkale UniversityDenizliTurkey
  2. 2.Department of Electricity and Energy, Denizli Vocational SchoolPamukkale UniversityDenizliTurkey
  3. 3.Department of Electrical and Electronics EngineeringPamukkale UniversityDenizliTurkey

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