Comparison of artificial neural network adaptive control techniques for a nonlinear system with delay
Systems and Control
2023-04-27 v1 Systems and Control
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.
Cite
@article{arxiv.2304.13468,
title = {Comparison of artificial neural network adaptive control techniques for a nonlinear system with delay},
author = {Bartłomiej Guś and Jakub Możaryn},
journal= {arXiv preprint arXiv:2304.13468},
year = {2023}
}
Comments
Submitted to MMAR 2023 - 27th International Conference on Methods and Models in Automation and Robotics