グラフ探索と機械学習に基づく二輪走行車両のモデル予測制御器設計

カテゴリ: 論文誌(論文単位)

グループ名: 【C】電子・情報・システム部門

発行日: 2013/02/01

タイトル(英語): Model Predictive Controller Design for Two-wheeled Vehicle based on Graph Search and Machine Learning

著者名: 戸石　大輔（名城大学理工学部情報工学科），小中　英嗣（名城大学理工学部情報工学科）

著者名(英語): Daisuke Toishi (Department of Information Engineering, Faculty of Science and Technology, Meijo University), Eiji Konaka (Department of Information Engineering, Faculty of Science and Technology, Meijo University)

キーワード: グラフ探索，サポートベクターマシン，二輪走行車両　　Graph search，support vector machine，two-wheeled vehicle

要約(英語): The configuration of a two-wheeled vehicle, such as Segway, cannot be stabilized by continuous and time-invariant state feedback due to its non-holonomic constraints. Because of the nonlinear nature of the nonholonomic constraints, the realization of a model predictive control (MPC) for this class of vehicles is a difficult task.This paper proposes a MPC method that can achieve long prediction horizon and quick computation. At the first step, the optimization of an input (i.e., velocity and steering) sequence is formulated as a graph search problem by restricting the inputs to discrete values. Next, in the second step, the optimized control result is learned by machine learning method, such as SVM.A longer horizon MPC compared to that with nonlinear optimization can be realized. The advantages of the proposed method are demonstrated with simulation and experimental results.

本誌: 電気学会論文誌C（電子・情報・システム部門誌） Vol.133 No.2 （2013） 特集：省電力時代の電子回路技術

本誌掲載ページ: 342-349 p

原稿種別: 論文／日本語

電子版へのリンク: https://www.jstage.jst.go.jp/article/ieejeiss/133/2/133_342/_article/-char/ja/