• The Avdanced Control Systems Lab
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The Advanced Control Systems Lab


The primary goal of the Advanced Control Systems Lab (ACSL) is to perform research on advanced topics in modern control theory, such as nonlinear robust control, nonlinear estimation, differential game theory, and optimal control theory. Our theoretical results are tested by designing autonomous control systems for unmanned aerial vehicles.

ACSL is a unique research facility comprised of a 6400 square foot hangar, where Dr. L'Afflitto and his collaborators perform indoor operations involving ground and aerial robots. This facility is equipped with a state-of-the-art Vicon motion capture system to measure position, attitude, and velocity of the vehicles being tested. Our research partners include, but are not limited to, NOAA, ARL through a CRADA, ORNL through a MOU, CASS, NWC, and OU Center for Intelligence and National Security.

Research Areas

At ACSL, we create, code, test, and implement nonlinear robust guidance and control laws. Multi-rotor UAS provide unique test-beds for our results. Here is how our team is organized:

Advanced Control Systems and Autopilots Design

Design of innovative nonlinear robust adaptive output-feedback control laws. These control laws are applied to design autopilots for multi-rotor aircraft that guarantee satisfactory results despite model uncertainties, motor failures, and unsteady payloads.

Students: Robert B. Anderson (AE) and Elizabeth A. Moffat (Phys/AE)

Robust Adaptive Control in Presence of Constraints

Design of robust adaptive controls for trajectory following in the presence of constraints on the state space. Our control laws are effective also in case the reference signals violate these constraints and hence, may draw the plant state outside the constraint set. Applying these results to UAS, physical constraints are detected using SLAM algorithms.

Students: Timothy A. Blackford and Joshua Karinshak (ME/CS)

Robotic Manipulators and Octocopters

Design of robotic manipulators to be installed on autonomous UAS, whose task is to to manipulate objects. Original robust adaptive controls are created to design autopilots for these platforms.

Students: Ann Broostein (ME) and Joshua Karinshak (ME/CS)

Numerical Robust Optimal Control

Developing a guidance system for autonomous UAS based on a fast Model Predictive Control algorithm. This algorithms will be able to account for non-convex constraints and sufficiently fast to allow fast flights in cluttered unknown environments.

Student: Timothy A. Blackford (AE)

Aerial Robotics and Octocopter Design

Design of aerial platforms used for object manipulation, which are capable of thrust vectoring. Original robust adaptive controls are created and utilized to design autopilots for these platforms.

Students: Blake Herren (AE), John-Paul Burke (AE), Karen Martinez-Soto (AE), Glenn L. Medina (AE), and Noah C. Golly (ME).

Virtual Reality for UAS Testing

Developing a virtual-reality simulator based on the Microsoft AirSim simulator. Out ultimate goal is to produce a high-fidelity simulator, which will help testing and tuning autopilots for quadrotors. This research is performed in collaboration with the Aerial Informatics and Robotics (AIR) group at Microsoft.

Student: Coleton Domann (AE), Julius Marshall (AE), and Joshua Karinshak (ME/CS)

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.

Student: Joshua J. Hughes (ME) and Zach T. Watkins (ME)

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 statistical analysis of flight tests and deduce the quality of our autopilots.

Student: Geetanjali Ghanta (ISE), Karen Martinez-Sonto (AE), and Lauren Ingmire (AE)

Selected Technological Products

We believe that sharing knowledge and technology is fundamental for the progress of science and technology. In the following, you find a selection of the computer codes produced at ACSL that we are pleased to share with everyone.

This Video shows the first outcome of a joint project with the Microsoft Aerial Informatics and Robotics Group, wherein AirSim is modified to implement adaptive sliding-mode control, simulate motor faults, and account for the vehicle's full set of nonlinear equations of motion.

This project started in Fall 2017 and is being performed by Coleton Domann with the assistance of Robert B. Anderson.

This Simulink toolbox is a CAD-based simulator for a quadtoror landing on a ship at sea; The aircraft can be controlled by a 4-axis joystick. This toolbox allows to compare and constrasts the ability of an adaptive controller and a PID-based controller to help pilots landing quadrotors despite the ship's movement, the wind, and the failure of one propeller.

This code was produced over Summer 2017 by Coleton Domann.

This Simulink toolbox provides a working example of how multiple AR Drone Parrot 2.0 quadrotors can perform autonomous trajectory tracking by using Matlab 2015a and a Vicon system for motion capture. To see this toolbox in action, see this YouTube video.

This code was produced over Spring 2016 by E. Eudaley, R. B. Anderson, and K. S. Murray Jr.

This Simulink toolbox allows to compare any two control algorithms for quadrotors simultaneously. The vehicles are not modeled by ordinary differential equations, but by mean of a CAD model. PID- and MRAC-based autopilots are provided as working examples.

This code was produced over Fall 2016 as part of a student-centered learning activity for the Dr. L'Afflitto's "Flight Controls" class and has been improved by K. S. Murray Jr. and R. B. Anderson.

This Simulink toolbox provides a working example of how an AR Drone Parrot 2.0 can perform autonomous trajectory tracking by using Matlab 2015a and a Vicon system for motion capture.

This code was produced over Summer 2016 by Mr. R. B. Anderson.

Open Positions

Ph.D. Student

I would like to collaborate with a motivated student, who has good analytical skills and a strong background in mathematics and/or linear dynamical systems. Master of science in aerospace, mechanical, electrical engineering, mathematics, or closely related fields are preferable but not required. Applications from US citizens or permanent residents is strongly encouraged.

Undergraduate Students

I would like to collaborate with a motivated undergraduate student, who is interested in the design of advanced control algorithms using numerical methods. Proficiency in C or C++ and Matlab is preferable. This project is not limited to aerospace or mechanical engineering students only.

Undergraduate Students

I would like to collaborate with a motivated student, who is interested in the numerical of adaptive control techniques on quadcopters for automatic flight control problems. Proficiency in C or C++ and Matlab is preferable.

Interested candidates are invited to contact me via email.