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

Çetin M., Bahtiyar B., BEYHAN S.

Neural Computing and Applications, vol.31, pp.1029-1043, 2019 (SCI-Expanded) identifier

  • Publication Type: Article / Article
  • Volume: 31
  • Publication Date: 2019
  • Doi Number: 10.1007/s00521-017-3068-7
  • Journal Name: Neural Computing and Applications
  • Journal Indexes: Science Citation Index Expanded (SCI-EXPANDED), Scopus
  • Page Numbers: pp.1029-1043
  • Keywords: Adaptive neural network, Chebyshev polynomial network, Model predictive control, Real-time control, Stability, Three-tank liquid-level system, Uncertainty compensation
  • Yıldız Technical University Affiliated: No

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.