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Neural Computing and Applications
Erschienen in: Neural Computing and Applications 2/2019

21.06.2017 | Original Article

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

verfasst von: Meriç Çetin, Bedri Bahtiyar, Selami Beyhan

Erschienen in: Neural Computing and Applications | Sonderheft 2/2019

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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.
Vorheriger Artikel Recursive least-squares temporal difference learning for adaptive traffic signal control at intersection
Nächster Artikel Generalization improvement for regularized least squares classification

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Metadaten
Titel
Adaptive uncertainty compensation-based nonlinear model predictive control with real-time applications
verfasst von
Meriç Çetin
Bedri Bahtiyar
Selami Beyhan
Publikationsdatum
21.06.2017
Verlag
Springer London
Erschienen in
Neural Computing and Applications / Ausgabe Sonderheft 2/2019
Print ISSN: 0941-0643
Elektronische ISSN: 1433-3058
DOI
https://doi.org/10.1007/s00521-017-3068-7

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