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arXiv · 2304.13468

Comparison of artificial neural network adaptive control techniques for a nonlinear system with delay

Bartłomiej Guś·Jakub Możaryn
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Abstract

This research paper compares two neural-network-based adaptive controllers, namely the Hybrid Deep Learning Neural Network Controller (HDLNNC) and the Adaptive Model Predictive Control with Nonlinear Prediction and Linearization along the Predicted Trajectory (AMPC-NPLPT), for controlling a nonlinear object with delay. Specifically, the study investigates the effect of delay on the accuracy of the two controllers. The experimental results demonstrate that the AMPC-NPLPT approach outperforms HDLNNC regarding control accuracy for the given nonlinear object control problem.

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BibTeXRIS

Bartłomiej Guś, Jakub Możaryn. 2023-04-26. Comparison of artificial neural network adaptive control techniques for a nonlinear system with delay. https://arxiv.org/abs/2304.13468

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