Abstract:

Rapid advances in material science and additive manufacturing are enabling a new class of adaptive autonomous systems, such as morphing wing drones and soft robotic swimmers. These systems can adapt their shape to uncertain and rapidly changing flow conditions and are likely to provide superior performance compared to conventional rigid system, as demonstrated by the incredible performance of birds and insects that exploit the flexibility of their wings to perform agile and efficient flight maneuvers. Several challenges remain in understanding how these adaptive wings are designed and optimized to achieve system-level performance gains, and how efficient control of the nonlinear fluid-structure dynamics is achieved. In particular, the intertwined processes of optimizing the wing design and learning a control policy presents a great challenge.

In this talk, I will discuss ideas around co-design optimization and data-driven modeling and control, with applications in adaptive structures and morphing wings. I will introduce the sparse identification of nonlinear dynamics (SINDy) method, and recent work on extending the SINDy method using ensemble learning. I will show that the ensemble statistics from Ensemble-SINDy can be used for active learning, improved model predictive control, and sample efficient reinforcement learning.