158 citations · 160 across the 12 of their papers we have counts for
14 papers
Heterogeneous Multi-Agent Multi-Target Tracking using Cellular Sheaves
Tyler Hanks, Cristian F. Nino, Joana Bou Barcelo +3
Multi-agent target tracking in the presence of nonlinear dynamics and agent heterogeneity, where state-space dimensions may differ, is a challenging problem that traditional graph…
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…
Resilient State Recovery using Prior Measurement Support Information
Yu Zheng, Olugbenga Moses Anubi, Warren E. Dixon
Resilient state recovery of cyber-physical systems has attracted much research attention due to the unique challenges posed by the tight coupling between communication, computation…
Collaborative Indirect Influencing and Control on Graphs using Graph Neural Networks
Max L. Gardenswartz, Brandon C. Fallin, Cristian F. Nino +1
This paper presents a novel approach to solving the indirect influence problem in networked systems, in which cooperative nodes must regulate a target node with uncertain dynamics…
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…
Online ResNet-Based Adaptive Control for Nonlinear Target Tracking
Cristian F. Nino, Omkar Sudhir Patil, Jordan C. Insinger +2
A generalized ResNet architecture for adaptive control of nonlinear systems with black box uncertainties is developed. The approach overcomes limitations in existing methods by inc…
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…
Integral Concurrent Learning: Adaptive Control with Parameter Convergence without PE or State Derivatives
Anup Parikh, Rushikesh Kamalapurkar, Warren E. Dixon
Concurrent learning is a recently developed adaptive update scheme that can be used to guarantee parameter convergence without requiring persistent excitation. However, this techni…