3 research outputs found
Lyapunov-Based Dropout Deep Neural Network (Lb-DDNN) Controller
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
Composite Adaptive Lyapunov-Based Deep Neural Network (Lb-DNN) Controller
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 based on tracking error, and offer convergence guarantees
on only the tracking error without providing conclusions on the parameter
estimation performance. Motivated to provide guarantees on the DNN parameter
estimation performance, this paper provides the first result on composite
adaptation for adaptive Lyapunov-based DNN controllers, which uses the Jacobian
of the DNN and a prediction error of the dynamics that is computed using a
novel method involving an observer of the dynamics. A Lyapunov-based stability
analysis is performed which guarantees the tracking, observer, and parameter
estimation errors are uniformly ultimately bounded (UUB), with stronger
performance guarantees when the DNN's Jacobian satisfies the persistence of
excitation (PE) condition. Comparative simulation results demonstrate a
significant performance improvement with the developed composite adaptive
Lb-DNN controller in comparison to the tracking error-based Lb-DNN