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A Data-Driven Approach for Inverse Optimal Control by Liang, Zihao; Hao, Wenjian; Mou, Shaoshuai is a scholarly article available to read on EtoBox.

What is A Data-Driven Approach for Inverse Optimal Control about?

This paper proposes a data-driven, iterative approach for inverse optimal control (IOC), which aims to learn the objective function of a nonlinear optimal control system given its states and inputs. The approach solves the IOC problem in a challenging situation when the system dynamics is unknown. The key idea of the proposed approach comes from the deep Koopman representation of the unknown system, which employs a deep neural network to represent observables for the Koopman operator. By assuming the objective function to be learned is parameterized as a linear combination of features with unknown weights, the proposed approach for IOC is able to achieve a Koopman representation of the unknown dynamics and the unknown weights in objective function together. Simulation is provided to verify the proposed approach.

Author

Liang, Zihao; Hao, Wenjian; Mou, Shaoshuai

Published

2023

Language

EN