Jove
Visualize
联系我们
JoVE
x logofacebook logolinkedin logoyoutube logo
关于 JoVE
概览领导团队博客JoVE 帮助中心
作者
出版流程编辑委员会范围与政策同行评审常见问题投稿
图书馆员
用户评价订阅访问资源图书馆顾问委员会常见问题
研究
JoVE JournalMethods CollectionsJoVE Encyclopedia of Experiments存档
教育
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab Manual教师资源中心教师网站
使用条款与条件
隐私政策
政策

相关概念视频

Time-Domain Interpretation of PD Control01:07

Time-Domain Interpretation of PD Control

85
Proportional-Derivative (PD) control is a widely used control method in various engineering systems to enhance stability and performance. In a system with only proportional control, common issues include high maximum overshoot and oscillation, observed in both the error signal and its rate of change. This behavior can be divided into three distinct phases: initial overshoot, subsequent undershoot, and gradual stabilization.
Consider the example of control of motor torque. Initially, a positive...
85
PD Controller: Design01:26

PD Controller: Design

202
In automotive engineering, car suspension systems often employ Proportional Derivative (PD) controllers to enhance performance. PD controllers are utilized to adjust the damping force in response to road conditions. A controller, acting as an amplifier with a constant gain, demonstrates proportional control, with output directly mirroring input.
Designing a continuous-data controller requires selecting and linking components like adders and integrators, which are fundamental in Proportional,...
202
PI Controller: Design01:24

PI Controller: Design

224
Proportional Integral (PI) controllers are a fundamental component in modern control systems, widely used to enhance performance and mitigate steady-state errors. They are particularly effective in applications such as automatic brightness adjustment on smartphones, where they excel at mitigating steady-state errors for step-function inputs. Unlike PD controllers, which require time-varying errors to function optimally, PI controllers leverage their integral component to address residual...
224
Feedback control systems01:26

Feedback control systems

296
Feedback control systems are categorized in various ways based on their design, analysis, and signal types.
Linear feedback systems are theoretical models that simplify analysis and design. These systems operate under the principle that their output is directly proportional to their input within certain ranges. For instance, an amplifier in a control system behaves linearly as long as the input signal remains within a specific range. However, most physical systems exhibit inherent nonlinearity...
296
Linear Approximation in Time Domain01:21

Linear Approximation in Time Domain

72
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,...
72
Time and frequency -Domain Interpretation of PI Control01:27

Time and frequency -Domain Interpretation of PI Control

113
Proportional-Integral (PI) controllers are essential in many control systems to improve stability and performance. They are commonly used in everyday devices like thermostats to enhance system damping and reduce steady-state error. When the zero in the controller's transfer function is optimally placed, the system benefits significantly in terms of stability and accuracy.
Acting as a low-pass filter, the PI controller slows the system's response and extends settling times. This requires...
113

您也可能阅读

相关文章

通过共同作者、期刊和引用图与本文相关的文章。

排序
Same author

Estimating Distributions of Parameters in Nonlinear State Space Models with Replica Exchange Particle Marginal Metropolis-Hastings Method.

Entropy (Basel, Switzerland)·2022
Same author

Data-Driven Analysis of Nonlinear Heterogeneous Reactions through Sparse Modeling and Bayesian Statistical Approaches.

Entropy (Basel, Switzerland)·2021
查看所有相关文章

相关实验视频

Updated: Jun 14, 2025

An Experimental Platform to Study the Closed-loop Performance of Brain-machine Interfaces
10:51

An Experimental Platform to Study the Closed-loop Performance of Brain-machine Interfaces

Published on: March 10, 2011

13.7K

动态系统的概率估计和控制使用粒子过器与自适应逆向采样.

Taketo Omi1, Toshiaki Omori1,2

  • 1Department of Electrical and Electronic Engineering, Graduate School of Engineering, Kobe University, 1-1 Rokkodai-cho, Nada-ku, Kobe 657-8501, Japan.

Entropy (Basel, Switzerland)
|August 29, 2024
PubMed
概括

这项研究引入了一个新的概率框架,使用粒子过器同时估计和控制非线性动态系统,即使有噪音数据. 该方法有效地处理复杂的动态和不确定性,以更好地理解和操纵系统.

关键词:
数据同化数据同化数据驱动的科学数据驱动的科学现代控制理论 现代控制理论非线性动力学的非线性动态统计机器学习是统计机器学习.

更多相关视频

Real-Time Proxy-Control of Re-Parameterized Peripheral Signals using a Close-Loop Interface
11:54

Real-Time Proxy-Control of Re-Parameterized Peripheral Signals using a Close-Loop Interface

Published on: May 8, 2021

4.3K
WheelCon: A Wheel Control-Based Gaming Platform for Studying Human Sensorimotor Control
08:18

WheelCon: A Wheel Control-Based Gaming Platform for Studying Human Sensorimotor Control

Published on: August 15, 2020

4.9K

相关实验视频

Last Updated: Jun 14, 2025

An Experimental Platform to Study the Closed-loop Performance of Brain-machine Interfaces
10:51

An Experimental Platform to Study the Closed-loop Performance of Brain-machine Interfaces

Published on: March 10, 2011

13.7K
Real-Time Proxy-Control of Re-Parameterized Peripheral Signals using a Close-Loop Interface
11:54

Real-Time Proxy-Control of Re-Parameterized Peripheral Signals using a Close-Loop Interface

Published on: May 8, 2021

4.3K
WheelCon: A Wheel Control-Based Gaming Platform for Studying Human Sensorimotor Control
08:18

WheelCon: A Wheel Control-Based Gaming Platform for Studying Human Sensorimotor Control

Published on: August 15, 2020

4.9K

科学领域:

  • * 非线性动力学与控制
  • * 计算神经科学 计算神经科学
  • * 时间序列分析

背景情况:

  • *从时间序列数据中估计和控制动态系统对于理解非线性行为至关重要.
  • * 现有的方法经常与杂的观测和复杂系统固有的非线性作斗争.
  • *准确的状态估计和控制对于从物理学到生物学等应用至关重要.

研究的目的:

  • * 开发一个统一的概率框架,用于同时估计和控制非线性动态系统的状态.
  • * 应对杂的观测和潜在状态的不确定性所带来的挑战.
  • * 证明框架对各种非线性系统的有效性.

主要方法:

  • * 建议使用粒子过器进行概率框架.
  • *颗粒过器具有双重作用:状态/动态估计器和控制器.
  • * 该方法使用洛伦茨混乱系统和莫里斯-莱卡尔神经元模型进行了验证.

主要成果:

  • * 拟议的框架成功地估计和控制非线性动态系统.
  • *颗粒过器有效地管理系统的非线性和状态不确定性.
  • * 混沌和神经系统模型都显示出积极的结果.

结论:

  • *开发的概率框架为同时估计和控制提供了强大的解决方案.
  • *这种方法提高了理解和操纵复杂非线性动态的能力.
  • * 该方法在涉及时间序列数据的各种科学和工程领域具有前景.