Deep Learning for Robust Adaptive Inverse Control of Nonlinear Dynamic Systems: Improved Settling Time with an Autoencoder

An adaptive deep neural network is used in an inverse system identification setting to approximate the inverse of a nonlinear plant with the aim of constituting the plant controller by copying to the latter the weights and architecture of the converging deep neural network. This deep learning (DL) a...

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Veröffentlicht in:Sensors (Basel, Switzerland) Jg. 22; H. 16; S. 5935
Hauptverfasser: Alwan, Nuha A. S., Hussain, Zahir M.
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Basel MDPI AG 01.08.2022
MDPI
Schlagworte:
adaptive inverse control
Algorithms
autoencoder
Control systems
Controllers
Deep learning
Neural networks
nonlinear plant
Process controls
robust control
sensor control
Signal processing
United States
ISSN:1424-8220, 1424-8220
Online-Zugang:Volltext
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Zusammenfassung:An adaptive deep neural network is used in an inverse system identification setting to approximate the inverse of a nonlinear plant with the aim of constituting the plant controller by copying to the latter the weights and architecture of the converging deep neural network. This deep learning (DL) approach to the adaptive inverse control (AIC) problem is shown to outperform the adaptive filtering techniques and algorithms normally used in adaptive control, especially when in nonlinear plants. The deeper the controller, the better the inverse function approximation, provided that the nonlinear plant has an inverse and that this inverse can be approximated. Simulation results prove the feasibility of this DL-based adaptive inverse control scheme. The DL-based AIC system is robust to nonlinear plant parameter changes in that the plant output reassumes the value of the reference signal considerably faster than with the adaptive filter counterpart of the deep neural network. The settling and rise times of the step response are shown to improve in the DL-based AIC system.
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ISSN:1424-8220
1424-8220
DOI:10.3390/s22165935