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Inverse Optimal Control from Incomplete Trajectory Observations by Wanxin Jin; Dana Kulić; Shaoshuai Mou; Sandra Hirche is a Engineering article available to read on EtoBox.

What is Inverse Optimal Control from Incomplete Trajectory Observations about?

This article develops a methodology that enables learning an objective function of an optimal control system from incomplete trajectory observations. The objective function is assumed to be a weighted sum of features (or basis functions) with unknown weights, and the observed data is a segment of a trajectory of system states and inputs. The proposed technique introduces the concept of the recovery matrix to establish the relationship between any available segment of the trajectory and the weights of given candidate features. The rank of the recovery matrix indicates whether a subset of relevant features can be found among the candidate features and the corresponding weights can be learned from the segment data. The recovery matrix can be obtained iteratively and its rank non-decreasing property shows that additional observations may contribute to the objective learning. Based on the recovery matrix, a method for using incomplete trajectory observations to learn the weights of selected features is established, and an incremental inverse optimal control algorithm is developed by automatically finding the minimal required observation. The effectiveness of the proposed method is demon

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Author

Wanxin Jin; Dana Kulić; Shaoshuai Mou; Sandra Hirche

Publisher

SAGE Publications

Published

2021

Language

EN

Field

Engineering (Physical Sciences)