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-conne...
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| Format: | Article |
| Language: | English |
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IEEE
2025-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/11059245/ |
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| author | Omkar Sudhir Patil Duc M. Le Emily J. Griffis Warren E. Dixon |
| author_facet | Omkar Sudhir Patil Duc M. Le Emily J. Griffis Warren E. Dixon |
| author_sort | Omkar Sudhir Patil |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-31ba6b48edbb44799e526a6b68ba64d6 |
| institution | OA Journals |
| issn | 2169-3536 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-31ba6b48edbb44799e526a6b68ba64d62025-08-20T02:36:30ZengIEEEIEEE Access2169-35362025-01-011311794311795210.1109/ACCESS.2025.358425311059245Lyapunov-Based Deep Residual Neural Network (ResNet) Adaptive ControlOmkar Sudhir Patil0https://orcid.org/0000-0002-3820-2025Duc M. Le1https://orcid.org/0000-0003-2891-0439Emily J. Griffis2https://orcid.org/0009-0004-7684-1444Warren E. Dixon3https://orcid.org/0000-0002-5091-181XDepartment of Mechanical and Aerospace Engineering, University of Florida, Gainesville, FL, USADepartment of Mechanical and Aerospace Engineering, University of Florida, Gainesville, FL, USADepartment of Mechanical and Aerospace Engineering, University of Florida, Gainesville, FL, USADepartment of Mechanical and Aerospace Engineering, University of Florida, Gainesville, FL, USADeep 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.https://ieeexplore.ieee.org/document/11059245/Deep neural networksResNetsadaptive controlLyapunov-based methods |
| spellingShingle | Omkar Sudhir Patil Duc M. Le Emily J. Griffis Warren E. Dixon Lyapunov-Based Deep Residual Neural Network (ResNet) Adaptive Control IEEE Access Deep neural networks ResNets adaptive control Lyapunov-based methods |
| title | Lyapunov-Based Deep Residual Neural Network (ResNet) Adaptive Control |
| title_full | Lyapunov-Based Deep Residual Neural Network (ResNet) Adaptive Control |
| title_fullStr | Lyapunov-Based Deep Residual Neural Network (ResNet) Adaptive Control |
| title_full_unstemmed | Lyapunov-Based Deep Residual Neural Network (ResNet) Adaptive Control |
| title_short | Lyapunov-Based Deep Residual Neural Network (ResNet) Adaptive Control |
| title_sort | lyapunov based deep residual neural network resnet adaptive control |
| topic | Deep neural networks ResNets adaptive control Lyapunov-based methods |
| url | https://ieeexplore.ieee.org/document/11059245/ |
| work_keys_str_mv | AT omkarsudhirpatil lyapunovbaseddeepresidualneuralnetworkresnetadaptivecontrol AT ducmle lyapunovbaseddeepresidualneuralnetworkresnetadaptivecontrol AT emilyjgriffis lyapunovbaseddeepresidualneuralnetworkresnetadaptivecontrol AT warrenedixon lyapunovbaseddeepresidualneuralnetworkresnetadaptivecontrol |