Lyapunov-Based Dropout Deep Neural Network (Lb-DDNN) Controller
Abstract
Deep neural network (DNN)-based adaptive controllers can be used to compensate for unstructured uncertainties in nonlinear dynamic systems. However, DNNs are also very susceptible to overfitting and co-adaptation. Dropout regularization is an approach where nodes are randomly dropped during training to alleviate issues such as overfitting and co-adaptation. In this paper, a dropout DNN-based adaptive controller is developed. The developed dropout technique allows the deactivation of weights that are stochastically selected for each individual layer within the DNN. Simultaneously, a Lyapunov-based real-time weight adaptation law is introduced to update the weights of all layers of the DNN for online unsupervised learning. A non-smooth Lyapunov-based stability analysis is performed to ensure asymptotic convergence of the tracking error. Simulation results of the developed dropout DNN-based adaptive controller indicate a 38.32% improvement in the tracking error, a 53.67% improvement in the function approximation error, and 50.44% lower control effort when compared to a baseline adaptive DNN-based controller without dropout regularization.
Keywords
Cite
@article{arxiv.2310.19938,
title = {Lyapunov-Based Dropout Deep Neural Network (Lb-DDNN) Controller},
author = {Saiedeh Akbari and Emily J. Griffis and Omkar Sudhir Patil and Warren E. Dixon},
journal= {arXiv preprint arXiv:2310.19938},
year = {2023}
}