- Research Program Mentor
PhD candidate at Pennsylvania State University (Penn State)
Aerospace Robotics, Autonomous Systems, Control Systems, Guidance, Navigation & Control (GNC)
BioMy primary research interests are in robotic manipulation and vehicular autonomy. I would like to identify and control complex engineering systems by realizing their natural dynamics through data-driven analysis. One particular application area that interests me is robotic manipulation. This has led me to study tensegrity system dynamics in my M.S. thesis at Penn State. I am eager to continue learning and teaching at the cutting edge of technology and hope to extend my work in a direction that inspires others to do the same. As an avid rock and ice climber, I am continually humbled and inspired by movement in the mountains. From these experiences, I aim to identify robotic systems with human-like abilities, primarily to aid us in our exploration efforts. In my spare time, I'm usually tinkering with vehicle hardware and embedded systems. Through (sometimes painful) quarrels with the hardware, I ended up building an autonomous UAV that I hope to continue improving for eventual use in Search & Rescue settings.
Aerospace Attitude Control
To maintain stable flight, aerospace vehicles are equipped with an attitude control system that corrects disturbances interacting with the vehicle’s dynamics. Stability is achieved by applying the forces that are necessary to return to a nominal trajectory through the vehicle’s actuators. Aerospace actuators come in many different shapes and sizes, but the fundamental theory behind attitude control is universal to nearly all aerospace vehicles. In this project, we’ll design a rudimentary attitude control system by focusing on the vehicle’s roll dynamics only. Your task is to architect a feedback control system to maintain level ‘flight’ about a quadrotor’s roll axis. We’ll begin by reviewing the equations of motion for a simple quadrotor model. Next, we’ll investigate and implement an industry-standard control method known as PID control. While using MATLAB to simulate and control the vehicle, we’ll discuss real-world hardware such as inertial measurement units (IMUs) and brushless (BLDC) motor controllers. Time-permitting, students will have the opportunity to investigate a new and exciting application of data-driven control (sometimes referred to as machine-learning control) through their quadrotor model. To learn more about the project and the fundamentals of control theory, please visit https://natesosikowicz.wixsite.com/curriculumvitae/control-systems