2 citations · 2 across the 19 of their papers we have counts for
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Adaptive Control as an Information Gradient Flow: Excitation, Constraints, Energy, and Stochastic Learning
Omkar Sudhir Patil
Adaptive control is usually developed through Lyapunov stability, excitation, and parameter convergence, whereas neighboring fields describe learning using convexity, information g…
Decentralized Scalar Field Mapping using Gaussian Process
Hossein Papi, Muzaffar Qureshi, Kyle Volle +2
Decentralized Gaussian process (GP) methods provide a scalable framework for multi-agent scalar-field estimation by allowing each agent to maintain a local GP over its assigned sub…
Nesterov-Accelerated Concurrent Learning for Lyapunov-Based Deep Neural Networks
Hossein Papi, Omkar Sudhir Patil
Concurrent learning (CL) has recently been extended to Lyapunov-based deep neural networks (DNNs) to obtain parameter convergence under finite rather than persistent excitation. Ho…
Neural ODEs Meet Concurrent Learning: Stable Online Learning with Lyapunov Guarantees
Omkar Sudhir Patil
Neural ODEs learn dynamics from trajectory losses, but their adjoint gradients lack the regressor-times-parameter-error structure on which Lyapunov analyses of online adaptation re…
LyLA-Therm: Lyapunov-based Langevin Adaptive Thermodynamic Neural Network Controller
Saiedeh Akbari, Omkar Sudhir Patil, Warren E. Dixon
Thermodynamic principles can be employed to design parameter update laws that address challenges such as the exploration vs. exploitation dilemma. In this paper, inspired by the La…
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…
Lyapunov-Based Graph Neural Networks for Adaptive Control of Multi-Agent Systems
Brandon C. Fallin, Cristian F. Nino, Omkar Sudhir Patil +2
Graph neural networks (GNNs) have a message-passing framework in which vector messages are exchanged between graph nodes and updated using feedforward layers. The inclusion of dist…
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…
Distributed RISE-based Control for Exponential Heterogeneous Multi-Agent Target Tracking of Second-Order Nonlinear Systems
Cristian F. Nino, Omkar Sudhir Patil, Sage C. Edwards +1
A distributed implementation of a Robust Integral of the Sign of the Error (RISE) controller is developed for multi-agent target tracking problems with exponential convergence guar…
Collaborative Spacecraft Servicing under Partial Feedback using Lyapunov-based Deep Neural Networks
Cristian F. Nino, Omkar Sudhir Patil, Christopher D. Petersen +2
Multi-agent systems are increasingly applied in space missions, including distributed space systems, resilient constellations, and autonomous rendezvous and docking operations. A c…
Lyapunov-Based Deep Neural Networks for Adaptive Control of Stochastic Nonlinear Systems
Saiedeh Akbari, Cristian F. Nino, Omkar Sudhir Patil +1
Controlling nonlinear stochastic dynamical systems involves substantial challenges when the dynamics contain unknown and unstructured nonlinear state-dependent terms. For such comp…
Adaptive Deep Neural Network-Based Control Barrier Functions
Hannah M. Sweatland, Omkar Sudhir Patil, Warren E. Dixon
Safety constraints of nonlinear control systems are commonly enforced through the use of control barrier functions (CBFs). Uncertainties in the dynamic model can disrupt forward in…
Lyapunov-Based Deep Residual Neural Network (ResNet) Adaptive Control
Omkar Sudhir Patil, Duc M. Le, Emily J. Griffis +1
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 adaptiv…
Simultaneous Online System Identification and Control using Composite Adaptive Lyapunov-Based Deep Neural Networks
Omkar Sudhir Patil, Emily J. Griffis, Wanjiku A. Makumi +1
Although deep neural network (DNN)-based controllers are popularly used to control uncertain nonlinear dynamic systems, most results use DNNs that are pretrained offline and the co…
Lyapunov-Based Dropout Deep Neural Network (Lb-DDNN) Controller
Saiedeh Akbari, Emily J. Griffis, Omkar Sudhir Patil +1
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…
Adaptive Control of Time-Varying Parameter Systems with Asymptotic Tracking
Omkar Sudhir Patil, Runhan Sun, Shubhendu Bhasin +1
A continuous adaptive control design is developed for nonlinear dynamical systems with linearly parameterizable uncertainty involving time-varying uncertain parameters. The key fea…