Projects
Projects
Stable Machine Learning for Prediction and Control Data-Driven Control of Robotic Fish Real-Time Control for Nonlinear Systems
Research
In the Interactive and Emergent Autonomy Lab , my research focuses on algorithmic development and computational methods for real-time system identification and nonlinear (model-based and data-driven) control of underwater robotics. This work often involves
- Mathematical modeling
- Development of new numerical tools that can be run in real-time
- Advancement of machine learning tools
- Algorithmic implementation and programming, and
- Experimentation
Stable Machine Learning for Prediction and Control
This project focuses on developing machine learning techniques that improve both short- and long-term prediction accuracy. We develop numerical methods that guarantee the stability of data-driven modeling, prediction, and control and can run in real time. I test these methods in simulations and experiments on various robotic systems and applications, including predicting the gait cycle of a biped, stabilizing a quadrotor, and tracking trajectories or pushing blocks using the Franka Emika robot.
Data-Driven Control of Robotic Fish
This project focuses on real-time system identification and data-driven control of robotic fish. I developed a systematic, data-driven methodology that creates linear representations of nonlinear systems that bounds the model accuracy and enables real-time control synthesis. In collaboration with Michigan State University, I tested the approach on tail-actuated robotic fish and show that it ourperforms alternative well-tuned feedback schemes.