• Intuition...
  • ...studying...
  • ...formalizing ideas...
  • ...and veryfing the results

Ongoing Research Projects

GNC System for UAS Moving in Tactical Terrain

In this research, we design an integrated guidance, navigation, and control sytstem for autopilots of multi-rotor Unmanned Aerial Systems (UAS) operating in potentially hostile areas.

Sponsor: DARPA

Collaborator: Eric Johnson, Penn State University

Robust Data-Driven Control of Unmanned Aerial Systems

Robust data-driven control for autonomous control of UAS are created, implemented, and testeded. Our controllers also assist human operators both to navigate in cluttered and poorly modeled environments and to prevent dangerous maneuvers.

Collaborators: Army Research Lab

Sponsor: RCTA

Constrained Control Design and UAS

Nonlinear robust control techniques for constrained dynamical systems are designed and tested on UAS. Students from multiple departments at OU are involved.

Sponsor: National Science Foundation

Aerial robotics

In this research, we pursue the design of guidance, navigation, and control systems for multi-rotor UAS equipped with robotic arms to autonomosly find, pickup, manipulate, and deploy objects in unknown environments.

Past Projects

UAS and Improved Weather Services

In this research, we designed autopilots for UAS collecting data for improved weather forecasts. We also combined an indirect adaptive control law and an unscented Kalman filter to estimate the wind velocity from the effort needed to hover the vehicle.

Sponsor: NOAA

Optimal Control for Finite-Time Stabilization

It is often desireable to attain finite-time stability of a nonlinear system, that is, converging to a Lyapunov stable equilibrium point in finite time. In this research, we provided sufficient conditions for state- and output-feedback optimal finite-time stabilization. YouTube video.

Differential Games of Nonlinear Dynamical Systems

In this research, we studied two-player differential games whose end-of-game condition is the closed-loop asymptotic, partial-state, or finite-time stability of the closed-loop system despite the evader's input. Connections to robust nonlinear control are explored.

Output-Feedback Sliding Mode Control with Constraints on the State Space

Accounting for recent results in the theory of time-varying finite-time stable dynamical systems, in this reseach we designed sliding mode controls for nonlinear constrained dynamical systems. The proposed control law is effective with any observer.

Optimal Semistabilization of Linear and Nonlinear Dynamical Systems

In this research, we derived state-feedback control laws that minimize a performance measure in integral form and guarantee semistability of the closed-loop system. YouTube Videos: Video 1 and Video 2.

Research Thrusts

Robust Adaptive Control in Presence of Constraints

Design of robust adaptive controls for trajectory following in the presence of constraints on the state and control space. Our control laws are effective also in case the reference signals violate these constraints.

Fast Model Predictive Control

We are designing a fast model predictive control (MPC) algorithm to generate reference trajectories for nonlinear dynamical systems subject to constraints on the stete, the outpur, and the control input and affected by external disturbances. This algorithms will be employed on UAS flying in unstructured environment.

Analytical Modeling of Mechanical Systems

Overly accurate models significantly impact the computational cost of the nonlinear robust control laws. We are devising techniques to produce dynamical model that are sufficiently accurate and yet simple to design nonlinear robust control laws under stringent computational constraints.

Analytical modeling of UAS' thrust in proximity of hard surfaces

In proximity of hard surfaces, the thrust produced by UAS' propellers cannot be modeled as proportional to the square of the angular velocity. In this research, performed in collaboration with Dr. D. K. Walters, we model the thrust force of multi-rotor UAS close to walls, floors, ceilings, and in the presence of cross-wind. These models are used to improve robustness marging in our UAS autopilots.

Statistical analysis of UAS trajectories

The outcome of flight tests depends on numerous stochastic factors, which are out of our control, such as wind and sensors' noise. In this research, we perform in collaboration with Dr. Shima Mohebbi statistical analysis of flight tests and deduce the quality of our autopilots.