Lyapunov-Based Deep Residual Neural Network (ResNet) Adaptive Control
<|reference_start|>Lyapunov-Based Deep Residual Neural Network (ResNet) Adaptive Control: Deep Neural Network (DNN)-based controllers have emerged as a tool to compensate for unstructured uncertainties in nonlinear dynamical systems. A recent breakthrough in the adaptive control literature provides a Lyapunov-based approach to derive weight adaptation laws for each layer of a fully-connected feedforward DNN-based adaptive controller. However, deriving weight adaptation laws from a Lyapunov-based analysis remains an open problem for deep residual neural networks (ResNets). This paper provides the first result on Lyapunov-derived weight adaptation for a ResNet-based adaptive controller. A nonsmooth Lyapunov-based analysis is provided to guarantee asymptotic tracking error convergence. Comparative Monte Carlo simulations are provided to demonstrate the performance of the developed ResNet-based adaptive controller. The ResNet-based adaptive controller shows a 64% improvement in the tracking and function approximation performance, in comparison to a fully-connected DNN-based adaptive controller.<|reference_end|>
@article{patil2024lyapunov-based,
    title={Lyapunov-Based Deep Residual Neural Network (ResNet) Adaptive Control},
    author={Omkar Sudhir Patil, Duc M. Le, Emily J. Griffis, and Warren E. Dixon},
    journal={arXiv preprint arXiv:2404.07385},
    year={2024},
    archivePrefix={arXiv},
    eprint={2404.07385},
    primaryClass={eess.SY cs.SY}
}