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相关概念视频

Feedback control systems01:26

Feedback control systems

685
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...
685
Linear Approximation in Time Domain01:21

Linear Approximation in Time Domain

343
Nonlinear systems often require sophisticated approaches for accurate modeling and analysis, with state-space representation being particularly effective. This method is especially useful for systems where variables and parameters vary with time or operating conditions, such as in a simple pendulum or a translational mechanical system with nonlinear springs.
For a simple pendulum with a mass evenly distributed along its length and the center of mass located at half the pendulum's length,...
343
Classification of Systems-I01:26

Classification of Systems-I

549
Linearity is a system property characterized by a direct input-output relationship, combining homogeneity and additivity.
Homogeneity dictates that if an input x(t) is multiplied by a constant c, the output y(t) is multiplied by the same constant. Mathematically, this is expressed as:
549
Linear Approximation in Frequency Domain01:26

Linear Approximation in Frequency Domain

357
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....
357
State Space Representation01:27

State Space Representation

528
The frequency-domain technique, commonly used in analyzing and designing feedback control systems, is effective for linear, time-invariant systems. However, it falls short when dealing with nonlinear, time-varying, and multiple-input multiple-output systems. The time-domain or state-space approach addresses these limitations by utilizing state variables to construct simultaneous, first-order differential equations, known as state equations, for an nth-order system.
Consider an RLC circuit, a...
528
Control System Problem01:21

Control System Problem

404
In an open-loop system, such as a basic thermostat, the poles of the transfer function influence the system's response but do not determine its stability. However, when feedback is introduced to form a closed-loop system, such as an advanced thermostat that adjusts heating based on room temperature, stability is governed by the new poles of the closed-loop transfer function.
When forming a closed-loop system, issues can arise if the poles cross into the unstable region, leading to potential...
404

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相关实验视频

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Real-Time Proxy-Control of Re-Parameterized Peripheral Signals using a Close-Loop Interface
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一类采样数据非线性系统的基于模式的学习和控制.

Qinchen Yang1, Fukai Zhang1, Cong Wang1

  • 1School of control Science and Engineering, Shandong University, Jinan, 250000, China.

Neural networks : the official journal of the International Neural Network Society
|October 14, 2025
PubMed
概括
此摘要是机器生成的。

本研究为动态工业系统引入了一种新的基于模式的学习控制策略. 它使用神经网络和确定性学习来调整控制器以适应不断变化的条件,确保稳定和高性能运行.

关键词:
适应神经控制 适应神经控制确定性学习学习是决定性的.基于模式的控制方式采样数据非线性系统的数据.

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科学领域:

  • * 工业控制系统工程 * 工业控制系统工程
  • * 适应性控制理论
  • * 控制中的机器学习

背景情况:

  • * 传统的控制方案在动态的工业环境中失败,系统参数不断变化.
  • *采样数据系统需要先进的控制策略来管理多种操作场景.
  • *复杂性源于时间变化的动态和实时适应的需要.

研究的目的:

  • *为采样数据系统开发一个强大的基于模式的学习和控制策略.
  • * 应对工业过程中多个动态操作场景所带来的挑战.
  • * 为了提高系统稳定性和控制性能在不同的条件下.

主要方法:

  • *双阶段识别:设计采样数据神经网络 (NN) 控制器,并使用确定性学习 (DL) 理论构建候选控制器库.
  • *第二个识别阶段:使用估计器精确识别闭环系统动态.
  • *识别和控制阶段:通过最小残余原则快速检测场景变化,并选择合适的学习控制器.

主要成果:

  • *使用基于DL理论的NNs准确近似未知系统动态.
  • * 在正常控制器操作下有效识别系统动态.
  • * 快速准确地检测控制场景变化.
  • *成功选择合适的学习控制器,确保稳定性和性能.

结论:

  • * 提出的基于模式的学习控制策略有效地处理动态的工业过程.
  • *这种方法确保了系统稳定性和在多种操作场景中实现高性能控制.
  • *模拟结果验证了适应性控制方法的有效性.