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

Feedback control systems01:26

Feedback control systems

283
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...
283
Control Systems01:10

Control Systems

1.0K
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...
1.0K
Electro-mechanical Systems01:19

Electro-mechanical Systems

917
Electromechanical systems are intricate configurations that effectively combine electrical and mechanical elements to achieve a desired outcome. Central to many of these systems is the DC motor, a device that converts electrical energy into mechanical motion, enabling various applications ranging from simple fans to complex robotic mechanisms.
A key component of the DC motor is the armature, a rotating circuit positioned within a magnetic field. As an electric current passes through the...
917
Controller Configurations01:22

Controller Configurations

85
Controller configurations are crucial in a car's cruise control system because they manage speed over time to maintain a consistent pace regardless of road conditions, thereby meeting design goals. In traditional control systems, fixed-configuration design involves predetermined controller placement. System performance modifications are known as compensation.
Control-system compensation involves various configurations, most commonly series or cascade compensation, in which the controller...
85
Control Systems: Applications01:25

Control Systems: Applications

573
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...
573
Time-Domain Interpretation of PD Control01:07

Time-Domain Interpretation of PD Control

83
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...
83

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

Efficient data replication in distributed clouds via quantum entanglement algorithms.

MethodsX·2026
Same author

Natural fiber filaments transforming the future of sustainable 3D printing.

MethodsX·2025
Same author

Grey wolf optimization technique with U-shaped and capsule networks-A novel framework for glaucoma diagnosis.

MethodsX·2025
Same author

Enhanced leukemia prediction using hybrid ant colony and ant lion optimization for gene selection and classification.

MethodsX·2025
Same author

Enhanced diabetic retinopathy detection using U-shaped network and capsule network-driven deep learning.

MethodsX·2025
Same author

Abdomen (A Pandora's Box): A Delayed Presentation of Blunt Abdominal Injury With a Mesenteric Tear Leading to Gangrenous Bowel.

Cureus·2024

相关实验视频

Updated: Jun 5, 2025

Design and Application of a Fault Detection Method Based on Adaptive Filters and Rotational Speed Estimation for an Electro-Hydrostatic Actuator
06:45

Design and Application of a Fault Detection Method Based on Adaptive Filters and Rotational Speed Estimation for an Electro-Hydrostatic Actuator

Published on: October 28, 2022

1.6K

无传感器向量控制的感应电机驱动:通过自适应神经模糊推理系统集成的增强模型参考自适应系统来提高性能.

Govindharaj I1, Dinesh Kumar K1, Balamurugan S2

  • 1Department of Computer Science and Engineering, Vel Tech Rangarajan Dr.Sagunthala R&D Institute of Science and Technology, Tamil Nadu 600062, India.

MethodsX
|December 16, 2024
PubMed
概括

这项研究将自适应神经模糊推理系统 (ANFIS) 控制器与模型参考自适应系统 (MRAS) 集成,用于在无传感器感应电机 (IM) 驱动器中增强速度控制. 该ANFIS-MRAS方案提高了动态性能和稳定性,特别是在低速时.

关键词:
适应性神经模糊推理系统感应电机驱动器 感应电机驱动自适应神经模糊推理系统 (ANFIS) 与模型参考自适应系统 (MRAS) 的集成.负载变化 负载变化模型参考适应性系统模型估计旋转器转速的方法无传感器的感应电机 无传感器的感应电机控制速度的速度控制器

更多相关视频

The Modular Design and Production of an Intelligent Robot Based on a Closed-Loop Control Strategy
11:53

The Modular Design and Production of an Intelligent Robot Based on a Closed-Loop Control Strategy

Published on: October 14, 2017

11.6K
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

相关实验视频

Last Updated: Jun 5, 2025

Design and Application of a Fault Detection Method Based on Adaptive Filters and Rotational Speed Estimation for an Electro-Hydrostatic Actuator
06:45

Design and Application of a Fault Detection Method Based on Adaptive Filters and Rotational Speed Estimation for an Electro-Hydrostatic Actuator

Published on: October 28, 2022

1.6K
The Modular Design and Production of an Intelligent Robot Based on a Closed-Loop Control Strategy
11:53

The Modular Design and Production of an Intelligent Robot Based on a Closed-Loop Control Strategy

Published on: October 14, 2017

11.6K
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

科学领域:

  • 电气工程 电气工程
  • 控制系统 控制系统
  • 人工智能的人工智能

背景情况:

  • 无传感感应电机 (IM) 驱动器需要强大的速度控制,特别是在零和非常低的速度.
  • 模型参考适应系统 (MRAS) 提供有效性,但可能对参数不确定性和负载变化敏感.
  • 现有的控制方法在不同操作条件下保持稳定性和动态性能的挑战.

研究的目的:

  • 为了提高无传感器矢量控制的IM驱动器的弹性和动态性能.
  • 为了提高IM驱动器的速度跟踪精度和操作流性.
  • 为了减轻参数不确定性和外部干扰对控制系统的影响.

主要方法:

  • 适应性神经模糊推理系统 (ANFIS) 控制器与模型参考适应性系统 (MRAS) 的集成.
  • 使用ANFIS根据速度估计错误自适应调整控制器参数.
  • 实施用于感应电机 (IM) 驱动器的无传感器向量控制策略.

主要成果:

  • 与现有系统相比,ANFIS增强的MRAS表现出优越的动态性能和稳定性.
  • 改进了参考速度跟踪和更顺的驱动操作.
  • 观察到对参数变化的敏感性降低,例如电机参数和负载扭矩.

结论:

  • 拟议的ANFIS-MRAS方案是无传感器IM驱动器中精确控制速度的有效解决方案.
  • 集成显著提高了系统的稳定性和可靠性,特别是在具有挑战性的条件下.
  • 这种方法非常适合要求IM速度控制高精度和可靠性的应用.