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

PI Controller: Design01:24

PI Controller: Design

217
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...
217
Time and frequency -Domain Interpretation of PI Control01:27

Time and frequency -Domain Interpretation of PI Control

111
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...
111
PID Controller01:19

PID Controller

104
Proportional-Integral-Derivative (PID) controllers are widely used in various control systems to enhance stability and performance. In a thermostat, it adjusts heating or cooling based on the temperature difference between the actual and desired levels. They are often used in automotive speed systems, effectively managing sudden speed changes while maintaining a constant speed under varying conditions. On the other hand, PI controllers, commonly employed in voltage regulation, enhance stability...
104
Open and closed-loop control systems01:17

Open and closed-loop control systems

667
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...
667
Multi-input and Multi-variable systems01:22

Multi-input and Multi-variable systems

100
Cruise control systems in cars are designed as multi-input systems to maintain a driver's desired speed while compensating for external disturbances such as changes in terrain. The block diagram for a cruise control system typically includes two main inputs: the desired speed set by the driver and any external disturbances, such as the incline of the road. By adjusting the engine throttle, the system maintains the vehicle's speed as close to the desired value as possible.
In the absence...
100
Feedback control systems01:26

Feedback control systems

293
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...
293

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

Yield estimation and operational parameter evaluation of three crude oils using aspen HYSYS simulation-based.

Scientific reports·2026
Same author

Crude Oil Yield Estimation: Recent Advances and Technological Progress in the Oil Refining Industry.

Sensors (Basel, Switzerland)·2025
Same author

Spontaneous Colonic Diaphragmatic Pericardial Herniation Causing Colonic Obstruction and Right Ventricular Collapse.

JACC. Case reports·2025
Same author

Prediction of Solvent Composition for Absorption-Based Acid Gas Removal Unit on Gas Sweetening Process.

Molecules (Basel, Switzerland)·2024
Same author

Rectenna System Development Using Harmonic Balance and S-Parameters for an RF Energy Harvester.

Sensors (Basel, Switzerland)·2024
Same author

A novel neural network-based framework to estimate oil and gas pipelines life with missing input parameters.

Scientific reports·2024
Same journal

A tri-axis optomechanical accelerometer with plasmonic MIM waveguide and structural direction-dependent optical signatures.

Scientific reports·2026
Same journal

Holographic leaky-wave antennas with independently controlled multiple counter-rotating vortex beams.

Scientific reports·2026
Same journal

Differential associations of longitudinal hearing and vision trajectories with dementia and mild cognitive impairment in older adults.

Scientific reports·2026
Same journal

Abdominal obesity and leisure-time sedentary behavior in relation to gastroesophageal reflux disease risk: a prospective cohort study from the UK Biobank.

Scientific reports·2026
Same journal

Effect of nitrogen-rich COF incorporation on the structure and separation performance of polyamide nanofiltration membranes.

Scientific reports·2026
Same journal

Withanolide A inhibits hIAPP aggregation: An In silico, biophysical, and drosophila-based In vivo validation.

Scientific reports·2026
查看所有相关文章

相关实验视频

Updated: Jun 11, 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

2.4K

最佳代学习PI控制器用于SISO和MIMO流程,具有用于性能预测的机器学习验证.

M Nagarajapandian1, S Kanthalakshmi2, P Arun Mozhi Devan3

  • 1Department of Electronics and Instrumentation Engineering, Sri Ramakrishna Engineering College, Coimbatore, 641022, Tamil Nadu, India. nagarajapandian.m@srec.ac.in.

Scientific reports
|October 9, 2024
PubMed
概括
此摘要是机器生成的。

一个新的代学习控制器,用混合算法进行优化,以补偿截止时间PI,增强工业过程控制. 这种先进的控制器在单输入单输出和多输入多输出系统中显著提高了系统稳定性和响应时间.

关键词:
狮子优化 狮子优化代学习控制器控制器机器学习 机器学习多变量系统的多变量系统.控制 PI 控制 PI 控制这是一个四重坦克系统.模拟火的模拟火

更多相关视频

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
Gain-compensation Methodology for a Sinusoidal Scan of a Galvanometer Mirror in Proportional-Integral-Differential Control Using Pre-emphasis Techniques
09:01

Gain-compensation Methodology for a Sinusoidal Scan of a Galvanometer Mirror in Proportional-Integral-Differential Control Using Pre-emphasis Techniques

Published on: April 4, 2017

8.6K

相关实验视频

Last Updated: Jun 11, 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

2.4K
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
Gain-compensation Methodology for a Sinusoidal Scan of a Galvanometer Mirror in Proportional-Integral-Differential Control Using Pre-emphasis Techniques
09:01

Gain-compensation Methodology for a Sinusoidal Scan of a Galvanometer Mirror in Proportional-Integral-Differential Control Using Pre-emphasis Techniques

Published on: April 4, 2017

8.6K

科学领域:

  • 控制工程 控制工程 控制工程
  • 优化算法 优化算法
  • 机器学习应用 机器学习应用

背景情况:

  • 多变量流程在工业中至关重要,但由于动态变化和可变相互作用,因此难以控制.
  • 传统的比例整合 (PI) 控制器虽然简单,但却难以应对多输入多输出 (MIMO) 系统的复杂性.
  • 需要先进的控制策略来解决现有的工业过程控制的局限性.

研究的目的:

  • 为加强工业过程控制提出一个代学习控制器截止时间补偿PI (ILC-DPI).
  • 使用一种新的混合模拟化-狮优化 (SA-ALO) 算法进行控制器调整.
  • 使用机器学习 (ML) 验证控制器性能,用于系统响应预测.

主要方法:

  • 开发了一种新的ILC-DPI控制器,采用SA-ALO优化算法.
  • 在单输入单输出 (SISO) 和四重系统 (MIMO) 上模拟和实验测试了控制器.
  • 使用回归和集合树ML模型来预测基于错误值的系统响应.

主要成果:

  • 拟议的ILC-DPI控制器在模拟和实时实验中表现出卓越的性能.
  • ML模型准确地预测了实际的系统响应,验证了控制器的有效性.
  • 控制器将超标量减少了近一半,并改善了结算时间,在SISO过程中实现了29.96%的快速响应,在MIMO过程中达到14.61%.

结论:

  • SA-ALO优化的ILC-DPI控制器为工业过程提供了系统稳定性和稳健性的显著改进.
  • 机器学习技术为验证先进控制系统性能提供了有效的工具.
  • 开发的控制器为复杂的SISO和MIMO工业控制挑战提供了可行的解决方案.