3 papers
eess.SY2025
Lyapunov-Based Physics-Informed Deep Neural Networks with Skew Symmetry Considerations
Rebecca G. Hart, Wanjiku A. Makumi, Rushikesh Kamalapurkar +1
Deep neural networks (DNNs) are powerful black-box function approximators which have been shown to yield improved performance compared to traditional neural network (NN) architectu…
eess.SY2025
System Identification and Control Using Lyapunov-Based Deep Neural Networks without Persistent Excitation: A Concurrent Learning Approach
Rebecca G. Hart, Omkar Sudhir Patil, Zachary I. Bell +1
Deep Neural Networks (DNNs) are increasingly used in control applications due to their powerful function approximation capabilities. However, many existing formulations focus prima…
eess.SY2025
Bounds on Deep Neural Network Partial Derivatives with Respect to Parameters
Omkar Sudhir Patil, Brandon C. Fallin, Cristian F. Nino +2
Deep neural networks (DNNs) have emerged as a powerful tool with a growing body of literature exploring Lyapunov-based approaches for real-time system identification and control. T…