Current research interests in NSR include networked control of autonomous agents, vision-based estimation and control, Lyapunov-based nonlinear control, human-robot interaction, and human-assisted estimation, planning, and decision-making.


Research Topics:

Control of Networked Autonomous Systems under Network Constraints
Supported by the Air Force Office of Science Research (AFOSR)

By unifying techniques from control theory, graph theory, communication, and estimation technology, the research aims to systematically design networked control strategies for autonomous assets to perform cooperative tasks (e.g., navigation, surveillance, etc.) in a complex environment with various constraints, such as network connectivity constraints, sensor constraints, and communication bandwidth constraints.

Dynamic Emotional Behavior and Automation Reliability in the Human-Machine Social Network
Supported by the Air Force Office of Science Research (AFOSR)

​The overarching objective is to develop an assistive technology that optimized the performance of human-robot interaction (HRI) in the presence of uncertain automation reliability and human factors, such as cognitive workload and trust in the automation.

A Privileged Sensing Framework: Revolutionizing Human-Autonomy Integration
Supported by the Autonomy Research Pilot Initiative, Department of Defense

​Based on consequence-based privilege and confidence-based human sensing, the privileged sensing framework aims to incorporate insights into operator state, capabilities, and intention to optimally fuse inputs from both human and autonomy to provide better decisions in human-autonomy systems.


Connectivity Maintenance of networked systems

Multi-agent systems have recently emerged as an inexpensive and robust way of addressing a wide variety of tasks ranging from exploration, surveillance and reconnaissance, to cooperative construction and manipulation. The success of these stories relies on efficient information exchange and coordination between the members of the team. The main interest in this project to develop decentralized controllers for a group of autonomous agents to perform coordinated global tasks using local information.  

Vision-based Estimation and Control

Structure and Motion (SaM) estimation using a camera is a very well-known problem in robotics and computer vision research community. SaM estimation is important for robotic application such as vision-based urban navigation of an autonomous agent, manipulation of unknown and moving targets, or human-machine interaction applications. The objective in SaM estimation is to estimate the Euclidean geometry of the feature points as well as the relative motion between the camera and feature points.


Lyapunov-based Nonlinear Control

The research is focused on the development and application of a Lyapunov-based control methodology, which incorporates the full nonlinear system dynamics in the design and analysis without requiring the solution of the nonlinear equations of motion. Research efforts are specifically focused on adaptive, robust, and learning control designs for nonlinear systems to address issues related to uncertain nonlinear dynamics with limited or uncalibrated/corrupt sensor information.



These research projects are currently or were funded in parts by the University of Iowa, Air Force Research Lab (AFRL), and National Science Foundation (NSF).