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相关概念视频

Open and closed-loop control systems01:17

Open and closed-loop control systems

753
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...
753
Feedback control systems01:26

Feedback control systems

314
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...
314
Controller Configurations01:22

Controller Configurations

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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...
100
PD Controller: Design01:26

PD Controller: Design

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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,...
234
Transfer Function in Control Systems01:21

Transfer Function in Control Systems

499
The transfer function is a fundamental concept in the analysis and design of linear time-invariant (LTI) systems. It offers a concise way to understand how a system responds to different inputs in the frequency domain. It serves as a bridge between the time-domain differential equations that describe system dynamics and the frequency-domain representation that facilitates easier manipulation and analysis.
To derive the transfer function, consider a general nth-order linear time-invariant...
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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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The Modular Design and Production of an Intelligent Robot Based on a Closed-Loop Control Strategy
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自建切比舍夫模糊神经补充滑动模式控制及其应用

Juntao Fei, Lei Zhang, Yunmei Fang

    IEEE transactions on neural networks and learning systems
    |January 10, 2024
    PubMed
    概括

    对于主动功率过器 (APF) 的一种新的控制策略使用自构造的切比舍夫模糊循环神经网络 (SCCFRNN) 来有效地抑制波. 这种先进的方法提高了建模精度,并减少了控制负担,以获得更好的电源质量.

    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:

    • 主动功率过器 (APF) 对于减轻电力系统中的波扭曲至关重要.
    • 对APF中非线性动态的准确建模对于有效的和声抑制至关重要.
    • 现有的控制方法可能面临模型不确定性和计算复杂性的挑战.

    研究的目的:

    • 提出一种新的互补滑动模式 (CSM) 控制器,与自构造的切比舍夫模糊反复神经网络 (SCCFRNN) 集成,用于APF波抑制.
    • 通过使用SCCFRNNN.通过近似未知非线性项来提高APF动态的建模精度.
    • 通过智能近似来减少CSM控制器 (CSMC) 的控制负担.

    主要方法:

    • 开发一个能够通过自学算法自动结构学习的SCCFRNN.
    • 将SCCFRNN与CSM控制器集成,用于APF中的声抑制.
    • 使用适应性定律来实时调整SCCFRNN参数.
    • 利用模糊神经网络 (FNN),循环神经网络 (RNN) 和切比舍夫神经网络 (CNN) 的综合优势.

    主要成果:

    • 在APF模型中,SCCFRNN有效地近似了未知的非线性动态.
    • 拟议的CSM控制器与SCCFRNN展示了卓越的声抑制能力.

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  • 模拟和硬件实验验证控制策略的可行性和有效性.
  • 基于SCCFRNN的方法在精度和控制复杂性降低方面比传统方法具有优势.
  • 结论:

    • 拟议的基于SCCFRNN的CSM控制器是APF中波抑制的可行和优质解决方案.
    • SCCFRNN的自建性质提高了适应性,并减少了需要精确的先前系统知识的需求.
    • 这种智能控制方法有助于提高电源质量和系统效率.