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

Control Systems01:10

Control Systems

1.4K
Control systems are everywhere in contemporary society, influencing diverse applications from aerospace to automated manufacturing. These systems can be found naturally within biological processes, such as blood sugar regulation and heart rate adjustment in response to stress, as well as in man-made systems like elevators and automated vehicles. A control system is essentially a network of subsystems and processes that collaboratively convert specific inputs into desired outputs.
At the heart...
1.4K
Open and closed-loop control systems01:17

Open and closed-loop control systems

984
Control systems are foundational elements in automation and engineering. They are broadly categorized into open-loop and closed-loop systems. These classifications hinge on the presence or absence of feedback mechanisms, significantly influencing the system's performance, complexity, and application.
An open-loop control system operates without feedback from the output. It consists of two primary elements: the controller and the controlled process. The controller receives an input signal...
984
Conservation of Energy in Control Volume01:14

Conservation of Energy in Control Volume

908
Consider a turbine operating under steady-flow conditions. The control volume is drawn around the turbine, with fluid entering at one point and exiting at another. The turbine extracts energy from the fluid, which performs mechanical work (shaft work).
For steady flow systems, the time derivative of the stored energy becomes zero since there is no energy accumulation within the control volume. This simplifies the energy equation to:
908
Feedback control systems01:26

Feedback control systems

416
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...
416
Time-Domain Interpretation of PD Control01:07

Time-Domain Interpretation of PD Control

178
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...
178
Control Systems: Applications01:25

Control Systems: Applications

736
Electrical engineering plays a pivotal role in our daily lives, with control systems at the heart of many applications, from home appliances to sophisticated space shuttles. Control systems manage and regulate the behavior of devices and processes, ensuring they function safely, correctly, and efficiently.
In modern vehicles, control systems manage various functions to enhance performance and safety. The steering wheel and accelerator are primary inputs in a car's control system. The...
736

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

CauFinder: Steering Cell-State and Phenotype Transitions by Causal Disentanglement Learning.

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

Stratification of viral shedding patterns in saliva of COVID-19 patients.

eLife·2026
Same author

Predicting collective states of a star network using reservoir computing.

Chaos (Woodbury, N.Y.)·2025
Same author

Longitudinal antibody titers measured after COVID-19 mRNA vaccination can identify individuals at risk for subsequent infection.

Science translational medicine·2025
Same author

Lag-mediated control of explosive synchronization transitions in adaptive multilayer networks with higher-order interactions.

Chaos (Woodbury, N.Y.)·2025
Same author

Overcoming feature scarcity in complex system prediction: An alternative delay embedding.

Chaos (Woodbury, N.Y.)·2025
Same journal

Multiscale dynamics of special memristive ion channels in a neural circuit.

Chaos (Woodbury, N.Y.)·2026
Same journal

Symmetry-protected delay spectroscopy in oscillator networks.

Chaos (Woodbury, N.Y.)·2026
Same journal

Mesoscale community organization governs epidemic onset and spread in metapopulations.

Chaos (Woodbury, N.Y.)·2026
Same journal

Topological dependence of viral mutation spread in complex host-interaction networks.

Chaos (Woodbury, N.Y.)·2026
Same journal

Multifractal signatures of Hamiltonian chaos in Hyperion's rotational dynamics.

Chaos (Woodbury, N.Y.)·2026
Same journal

Exploring mechanisms for reversal of flow in tunicate hearts.

Chaos (Woodbury, N.Y.)·2026
查看所有相关文章

相关实验视频

Updated: Sep 9, 2025

Interactive and Visualized Online Experimentation System for Engineering Education and Research
08:35

Interactive and Visualized Online Experimentation System for Engineering Education and Research

Published on: November 24, 2021

2.6K

使用储计算的动态系统的自适应控制

Swarnendu Mandal1, Swati Chauhan2, Umesh Kumar Verma2

  • 1International Research Center for Neurointelligence (WPI-IRCN), The University of Tokyo, Tokyo, Japan.

Chaos (Woodbury, N.Y.)
|September 4, 2025
PubMed
概括
此摘要是机器生成的。

本研究介绍了一种数据驱动的方法,使用储计算来进行动态系统的自适应控制. 它允许精确控制目标状态,使用最小的训练数据,在模拟和现实世界的电子电路中进行验证.

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

4.7K
Experimental Investigation of the Hierarchical Control in DC Microgrids Using a Real-time Simulator
06:04

Experimental Investigation of the Hierarchical Control in DC Microgrids Using a Real-time Simulator

Published on: February 14, 2025

606

相关实验视频

Last Updated: Sep 9, 2025

Interactive and Visualized Online Experimentation System for Engineering Education and Research
08:35

Interactive and Visualized Online Experimentation System for Engineering Education and Research

Published on: November 24, 2021

2.6K
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

4.7K
Experimental Investigation of the Hierarchical Control in DC Microgrids Using a Real-time Simulator
06:04

Experimental Investigation of the Hierarchical Control in DC Microgrids Using a Real-time Simulator

Published on: February 14, 2025

606

科学领域:

  • 复杂的系统
  • 非线性动力学
  • 机器学习

背景情况:

  • 动态系统通常需要适应性控制策略来实现所需状态.
  • 储计算提供了一个强大的框架来处理来自复杂系统的时间序列数据.

研究的目的:

  • 为动态系统开发和演示数据驱动的自适应控制技术.
  • 利用储存器计算来预测系统参数和生成控制信号.
  • 通过各种系统吸引力和初始条件验证控制方案的有效性.

主要方法:

  • 使用储存器计算来训练从时间序列数据中预测系统参数的模型.
  • 根据预测的系统参数开发反控制信号.
  • 使用控制信号引导动态系统向目标状态.
  • 通过数值模拟验证方法,并在物理Rössler系统电路上实现.

主要成果:

  • 储计算方法成功地从时间序列数据中预测系统参数.
  • 开发的控制信号有效地将动态系统驱动到任意的目标吸引器.
  • 该方法在各种吸引器类型和初始条件中显示出强度.
  • 在Rössler系统电子电路上成功实施证实了其实际可用性.

结论:

  • 拟议的数据驱动的自适应控制方法,由储计算提供,为动态系统提供一种高效和多功能方法.
  • 这种技术需要最少的训练数据,使其适用于现实世界.
  • 通过机器学习为复杂系统的先进控制策略铺平了道路.