Jove
Visualize
お問い合わせ
JoVE
x logofacebook logolinkedin logoyoutube logo
JoVEについて
概要リーダーシップブログJoVEヘルプセンター
著者向け
出版プロセス編集委員会範囲と方針査読よくある質問投稿
図書館員向け
推薦の声購読アクセスリソース図書館諮問委員会よくある質問
研究
JoVE JournalMethods CollectionsJoVE Encyclopedia of Experimentsアーカイブ
教育
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab Manual教員リソースセンター教員サイト
利用規約
プライバシーポリシー
ポリシー

関連する概念動画

State Space Representation01:27

State Space Representation

285
The frequency-domain technique, commonly used in analyzing and designing feedback control systems, is effective for linear, time-invariant systems. However, it falls short when dealing with nonlinear, time-varying, and multiple-input multiple-output systems. The time-domain or state-space approach addresses these limitations by utilizing state variables to construct simultaneous, first-order differential equations, known as state equations, for an nth-order system.
Consider an RLC circuit, a...
285
Root-Locus Method01:19

Root-Locus Method

213
A cruise control system in a car is designed to maintain a specified speed automatically by adjusting the gas pedal. The system continuously measures the vehicle's speed and makes fine adjustments to the pedal to achieve this goal. The root locus method is particularly useful for understanding how the cruise control system's behavior changes under varying conditions, such as when the car goes uphill, downhill, or faces strong wind resistance.
This system can be represented by a block...
213
One-Degree-of-Freedom System01:24

One-Degree-of-Freedom System

555
In mechanical engineering, one-degree-of-freedom systems form the basis of a wide range of electrical and mechanical components. Using these models, engineers can predict the behavior of various parts in a larger system, which gives them insight into how different forces interact with each other.
A one-degree-of-freedom system is defined by an independent variable that determines its state and behavior. One example of a one-degree-of-freedom system is a simple harmonic oscillator, such as a...
555
Linear Approximation in Time Domain01:21

Linear Approximation in Time Domain

124
Nonlinear systems often require sophisticated approaches for accurate modeling and analysis, with state-space representation being particularly effective. This method is especially useful for systems where variables and parameters vary with time or operating conditions, such as in a simple pendulum or a translational mechanical system with nonlinear springs.
For a simple pendulum with a mass evenly distributed along its length and the center of mass located at half the pendulum's length,...
124

こちらも読む

関連記事

共著者、ジャーナル、引用グラフによってこの研究に関連する記事。

並び替え
Same author

Classification-regression concatenated approach for simultaneous optical measurement of combustion smoke aerosol shape and particle size distribution.

Optics express·2026
Same author

Histotripsy treatment reduces tumor burden and extends survival in an orthotopic mouse model of osteosarcoma.

Frontiers in oncology·2026
Same author

MGDP: Mastering a Generalized Depth Perception Model for Quadruped Locomotion.

Advanced science (Weinheim, Baden-Wurttemberg, Germany)·2026
Same author

Superconducting Dome in La_{3-x}Sr_{x}Ni_{2}O_{7-δ} Thin Films.

Physical review letters·2026
Same author

Explosive Output to Enhance Jumping Ability: A Variable Reduction Ratio Design Paradigm for Humanoid Robot Knee Joint.

Biomimetics (Basel, Switzerland)·2026
Same author

S1PR3 mediates glial stimulated tumor invasion in response to interstitial fluid flow.

bioRxiv : the preprint server for biology·2026

関連する実験動画

Updated: Sep 10, 2025

Fully Automated Leg Tracking in Freely Moving Insects using Feature Learning Leg Segmentation and Tracking FLLIT
08:04

Fully Automated Leg Tracking in Freely Moving Insects using Feature Learning Leg Segmentation and Tracking FLLIT

Published on: April 23, 2020

6.9K

Proprioception を用いた ロボットのための リアルタイム カスケード状態推定フレームワーク

Botao Liu1, Fei Meng1, Zhihao Zhang1

  • 1School of Mechatronical Engineering, Beijing Institute of Technology, Beijing 100811, China.

Biomimetics (Basel, Switzerland)
|August 27, 2025
PubMed
まとめ
この要約は機械生成です。

この研究は,自感を用いた新しいロボット状態推定フレームワークを導入します. この方法は,特に足と地面の接触時に,足付きロボットの精度とリアルタイムのパフォーマンスを向上させます.

キーワード:
カルマンフィルター移動する地平線の推定自己受容州の見積もり

さらに関連する動画

Sit-to-stand-and-walk from 120% Knee Height: A Novel Approach to Assess Dynamic Postural Control Independent of Lead-limb
08:24

Sit-to-stand-and-walk from 120% Knee Height: A Novel Approach to Assess Dynamic Postural Control Independent of Lead-limb

Published on: August 30, 2016

10.3K
Engineering Platform and Experimental Protocol for Design and Evaluation of a Neurally-controlled Powered Transfemoral Prosthesis
11:16

Engineering Platform and Experimental Protocol for Design and Evaluation of a Neurally-controlled Powered Transfemoral Prosthesis

Published on: July 22, 2014

16.4K

関連する実験動画

Last Updated: Sep 10, 2025

Fully Automated Leg Tracking in Freely Moving Insects using Feature Learning Leg Segmentation and Tracking FLLIT
08:04

Fully Automated Leg Tracking in Freely Moving Insects using Feature Learning Leg Segmentation and Tracking FLLIT

Published on: April 23, 2020

6.9K
Sit-to-stand-and-walk from 120% Knee Height: A Novel Approach to Assess Dynamic Postural Control Independent of Lead-limb
08:24

Sit-to-stand-and-walk from 120% Knee Height: A Novel Approach to Assess Dynamic Postural Control Independent of Lead-limb

Published on: August 30, 2016

10.3K
Engineering Platform and Experimental Protocol for Design and Evaluation of a Neurally-controlled Powered Transfemoral Prosthesis
11:16

Engineering Platform and Experimental Protocol for Design and Evaluation of a Neurally-controlled Powered Transfemoral Prosthesis

Published on: July 22, 2014

16.4K

科学分野:

  • ロボット
  • 州の見積もり
  • 制御システム

背景:

  • 正確な状態の見積もりは ロボットの制御に不可欠です
  • 独自のセンサは,見積もりのための豊富な情報源を提供します.
  • 現存する方法は 移動中の衝撃のダイナミクスに 苦労しています

研究 の 目的:

  • ロボット用プロイオセプションのカスケード状態推定フレームワークを開発する.
  • ロボット状態の推定の精度とリアルタイムのパフォーマンスを向上させる.
  • ロボットが足で動いているときの 衝撃の音を効果的に処理する.

主な方法:

  • 汎用モメンタムベースのカルマンフィルター (GMKF) は,地面反応力を推定する.
  • エラー状態カルマンフィルター (ESKF) は,事前の状態の推定値を提供します.
  • 移動地平線推定 (MHE) の問題は,リー群で策定され,並列リアルタイムイテレーション (Para-RTI) で解決される.

主要な成果:

  • 提案されたフレームワークは,マニフォールドに緊密に結合された見積もりを実現します.
  • 既存の方法と比較して,より高い精度とリアルタイムのパフォーマンスを示しました.
  • 脚のあるロボットの足と地面との接触時の衝撃音を効果的に軽減します.

結論:

  • カスケードプロイオセプションベースのフレームワークはロボット状態の推定に優れたアプローチを提供します.
  • この方法は特に 動的環境で移動する 脚のロボットには有効です
  • BQR3 ロボットでの実験的な検証は,フレームワークの有効性を確認しています.