Quadrotor Trajectory Tracking with Learned Dynamics: Joint Koopman-based Learning of System Models and Function Dictionaries

Understand

Nonlinear dynamical effects are crucial to the operation of many agile robotic systems.

• Koopman-based model learning methods can capture these nonlinear dynamical system effects in higher dimensional lifted bilinear models that are amenable to optimal control.
• However, standard methods that lift the system state using a fixed function dictionary before model learning result in high dimensional models that are intractable for real time control.
• This paper presents a novel method that jointly learns a function dictionary and lifted bilinear model purely from data by incorporating the Koopman model in a neural network architecture.