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pro vyhledávání: '"Griffis, Emily J."'
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 weig
Externí odkaz:
http://arxiv.org/abs/2404.07385
Recent advancements in adaptive control have equipped deep neural network (DNN)-based controllers with Lyapunov-based adaptation laws that work across a range of DNN architectures to uniquely enable online learning. However, the adaptation laws are b
Externí odkaz:
http://arxiv.org/abs/2311.13056
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 approac
Externí odkaz:
http://arxiv.org/abs/2310.19938
Publikováno v:
In IFAC PapersOnLine 2023 56(2):6851-6856
