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

by   Ao Jin, et al.

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. An auxiliary neural network is developed to encode the nonlinear control term of finite dimensional lifted system. In addition, the state matrix A and control matrix B of lifted linear system in the embedding space are also estimated during training DNN. Then three loss functions that related to reconstruction and prediction ability of network and controllability of lifted linear system are designed for training the entire network. With the global linear system produced from DNN, Linear Quadratic Regulator (LQR) is applied to derive the optimal control policy for the TSR deployment. Finally, simulation results verify the effectiveness of proposed framework and show that it could deploy tethered space robot more quickly with less swing of in-plane angle.


page 2

page 7


Deep Koopman Operator with Control for Nonlinear Systems

Recently Koopman operator has become a promising data-driven tool to fac...

Learning Compositional Koopman Operators for Model-Based Control

Finding an embedding space for a linear approximation of a nonlinear dyn...

Towards Data-driven LQR with KoopmanizingFlows

We propose a novel framework for learning linear time-invariant (LTI) mo...

Learning-Based Optimal Control with Performance Guarantees for Unknown Systems with Latent States

As control engineering methods are applied to increasingly complex syste...

Data-Driven Distributionally Robust Optimal Control with State-Dependent Noise

This paper introduces innovative data-driven techniques for estimating t...

SOCKS: A Stochastic Optimal Control and Reachability Toolbox Using Kernel Methods

We present SOCKS, a data-driven stochastic optimal control toolbox based...

A Family of Iterative Gauss-Newton Shooting Methods for Nonlinear Optimal Control

This paper introduces a family of iterative algorithms for unconstrained...

Please sign up or login with your details

Forgot password? Click here to reset