Abstract
Nowadays, achieving efficient computations of optimal trajectories for dynamical systems represents a hard problem. In particular, the presence of nonlinearities and uncertainties affecting the outcome makes this task very challenging. With the objective of introducing optimal control strategies that address those difficulties, in this talk I will discuss two interrelated frameworks: Sequential Convex Programming (SCP) and Pullback Bundle Dynamical Systems (PBDS), from both theoretical and numerical perspectives. Specifically, I will first show how SCP can be leveraged to solve non-linear stochastic optimal control problems. Then, I will explain how efficient real-time initialization strategies can be compute by PBDS for complex dynamical systems.
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