Online Inverse Optimal Control on Infinite Horizons

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Molloy, Timothy L.
Ford, Jason J.
Perez, Tristan

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Institute of Electrical and Electronics Engineers Inc.

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Abstract

In this paper, we consider the problem of computing parameters of a discrete-time infinite-horizon optimal control objective function from (possibly finite-length) state and control sequences. To solve this problem, we propose a novel method of inverse optimal control by exploiting a recently established infinite-horizon discrete-time minimum principle. Our proposed method admits a computationally efficient online implementation in which pairs of states and controls from the state and control sequences are processed sequentially without being stored or processed as a batch. We establish conditions guaranteeing the uniqueness of the cost-function parameters computed by our proposed method and illustrate its application in simulation.

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2018 IEEE Conference on Decision and Control, CDC 2018

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