Data-Driven Optimal Control of Tethered Space Robot Deployment with Learning Based Koopman Operator

Understand

To avoid complex constraints of the traditional nonlinear method for tethered space robot (TSR) deployment, this paper proposes a data-driven optimal control framework with an improved deep learning based Koopman operator that could be applied to complex environments.

• In consideration of TSR's nonlinearity, its finite dimensional lifted representation is derived with the state-dependent only embedding functions in the Koopman framework.
• A deep learning approach is adopted to approximate the global linear representation of TSR.
• Deep neural networks (DNN) are developed to parameterize Koopman operator and its embedding functions.