Transactions of the Institute of Systems, Control and Information Engineers
Online ISSN : 2185-811X
Print ISSN : 1342-5668
ISSN-L : 1342-5668
Special Issue Paper
Evaluation of Temporal Deep Unfolding-Based MPC for Multilink Pendulum
Jumpei AizawaMasaki OguraMasako KishidaNaoki Wakamiya
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2023 Volume 36 Issue 4 Pages 91-98

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Abstract

In model predictive control (MPC), the control input at each time point is determined by solving an optimization problem. Being optimization-based, MPC is known for its limited applicability to systems with complex dynamics. This technical gap could be solved by the recently proposed MPC method based on temporal deep unfolding. Deep unfolding is method derived from deep learning, and it is used to solve an optimization problem. Temporal Deep Unfolding-Based MPC’s effectiveness is not yet thoroughly evaluated in the literature. Therefore, in this paper, we evaluate the effectiveness of the method for multilink pendulum systems by simulation.

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