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Title: A parallel Newton-type method for nonlinear model predictive control
Authors: Deng, Haoyang
Ohtsuka, Toshiyuki
Author's alias: 鄧, 昊洋
大塚, 敏之
Keywords: Nonlinear model predictive control
Parallel algorithm
Issue Date: Nov-2019
Publisher: Elsevier BV
Journal title: Automatica
Volume: 109
Thesis number: 108560
Abstract: A parallel Newton-type method for nonlinear model predictive control is presented that exploits the particular structure of the associated discrete-time Euler–Lagrange equations obtained by utilizing an explicit discretization method in the reverse-time direction. These equations are approximately decoupled into single-step subproblems along the prediction horizon for parallelization. The coupling variable of each subproblem is approximated to its optimal value using a simple, efficient, and effective method at each iteration. The rate of convergence of the proposed method is proved to be superlinear under mild conditions. Numerical simulation of using the proposed method to control a quadrotor showed that the proposed method is highly parallelizable and converges in only a few iterations, even to a high accuracy. Comparison of the proposed method’s performance with that of several state-of-the-art methods showed that it is faster.
Description: 並列計算を活用した実時間最適制御の高速アルゴリズムを開発. 京都大学プレスリリース. 2019-09-11.
Rights: © 2019 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY license (
DOI(Published Version): 10.1016/j.automatica.2019.108560
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