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相关概念视频

Feedback control systems01:26

Feedback control systems

270
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...
270
Open and closed-loop control systems01:17

Open and closed-loop control systems

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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...
605
Linear Approximation in Frequency Domain01:26

Linear Approximation in Frequency Domain

85
Linear systems are characterized by two main properties: superposition and homogeneity. Superposition allows the response to multiple inputs to be the sum of the responses to each individual input. Homogeneity ensures that scaling an input by a scalar results in the response being scaled by the same scalar.
In contrast, nonlinear systems do not inherently possess these properties. However, for small deviations around an operating point, a nonlinear system can often be approximated as linear....
85
Control Systems01:10

Control Systems

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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...
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PD Controller: Design01:26

PD Controller: Design

167
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,...
167
Root Loci for Positive-Feedback Systems01:23

Root Loci for Positive-Feedback Systems

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The Hartley oscillator is a positive feedback system that sustains oscillations by feeding the output back to the input in phase, thereby reinforcing the signal. Positive feedback systems can be viewed as negative feedback systems with inverted feedback signals. In these systems, the root locus encompasses all points on the s-plane where the angle of the system transfer function equals 360 degrees.
The construction rules for the root locus in positive feedback systems are similar to those in...
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Updated: May 25, 2025

Design and Application of a Fault Detection Method Based on Adaptive Filters and Rotational Speed Estimation for an Electro-Hydrostatic Actuator
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基于神经模糊网络的非线性混合主动噪声控制系统

Thi Trung Tin Nguyen1, Jing Na1, Le Thai Nguyen2

  • 1Faculty of Mechanical & Electrical Engineering, Kunming University of Science & Technology, Kunming 650500, China.

Entropy (Basel, Switzerland)
|February 26, 2025
PubMed
概括
此摘要是机器生成的。

本研究介绍了用于混合动力主动噪声控制 (HANC) 系统的自适应神经模糊网络 (ANFN) 控制器. 这种新方法在现实应用中提高了噪声抑制的有效性和稳定性.

关键词:
活动噪音控制 活动噪音控制混合动力主动噪声控制神经网络的神经网络的神经网络非线性波器是一种非线性波器.

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科学领域:

  • 声学和信号处理
  • 控制系统工程 控制系统工程
  • 在工程领域的人工智能.

背景情况:

  • 积极噪音控制 (ANC) 对于减轻环境噪音污染至关重要.
  • 现有的ANC系统在复杂环境中面临着强度和手动微调方面的挑战.
  • 混合动力主动噪声控制 (HANC) 集成了多种策略,以提高性能.

研究的目的:

  • 开发一种新的自适应神经模糊网络 (ANFN) 控制器,用于增强混合动力主动噪声控制 (HANC).
  • 提高主动噪声抑制的稳定性和有效性.
  • 解决非线性问题,减少复杂声学环境中的手动调整.

主要方法:

  • 一个自适应的神经网络被设计来最大限度地降低余噪的平均平方误差.
  • 一个模糊的逻辑策略被纳入处理环境非线性和减少手动调.
  • 拟议的基于ANFN的HANC控制器的稳定性使用Lyapunov定理被严格证明.

主要成果:

  • 数字模拟证明了拟议的基于ANFN的HANC方法的有效性.
  • 与现有方法相比,控制器在主动降噪方面表现优越.
  • 该系统在各种具有挑战性的噪声信号条件下证明了其稳健性.

结论:

  • 拟议的自适应性神经模糊网络控制器显著提高了混合动力主动噪声控制性能.
  • 这种基于ANFN的方法为现实世界噪音污染抑制提供了强大而有效的解决方案.
  • 该方法成功地克服了复杂环境中传统ANC系统的局限性.