First Lyapunov-based result for simultaneous online system identification and trajectory tracking of nonlinear systems via composite adaptive DNN updates for all layers, yielding UUB guarantees and exponential convergence under PE.
A theoretical framework for end- to-end learning of deep neural networks with applications to robotics
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Simultaneous Online System Identification and Control using Composite Adaptive Lyapunov-Based Deep Neural Networks
First Lyapunov-based result for simultaneous online system identification and trajectory tracking of nonlinear systems via composite adaptive DNN updates for all layers, yielding UUB guarantees and exponential convergence under PE.