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相关概念视频

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
Linear Approximation in Frequency Domain01:26

Linear Approximation in Frequency Domain

91
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....
91
Linear Approximation in Time Domain01:21

Linear Approximation in Time Domain

83
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,...
83
Linear time-invariant Systems01:23

Linear time-invariant Systems

262
A system is linear if it displays the characteristics of homogeneity and additivity, together termed the superposition property. This principle is fundamental in all linear systems. Linear time-invariant (LTI) systems include systems with linear elements and constant parameters.
The input-output behavior of an LTI system can be fully defined by its response to an impulsive excitation at its input. Once this impulse response is known, the system's reaction to any other input can be...
262
Effects of feedback01:24

Effects of feedback

564
Feedback in control systems plays a critical role in shaping various operational parameters, extending beyond simple error reduction to influence stability, bandwidth, gain, impedance, and sensitivity. Understanding these effects requires examining a basic feedback system characterized by defined input, output, error, and feedback signals.
Feedback significantly modifies the gain of a control system. The gain of a system without feedback is altered by a factor of one plus GH, where G represents...
564
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

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相关实验视频

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对不确定的非线性系统通过生成对抗网络进行反线性化控制.

Nuan Wen1, Zhenghua Liu1, Weihong Wang1

  • 1School of Automation Science and Electrical Engineering, Beihang University, 37 XueYuan Road, Haidian District, Beijing 100191, China.

ISA transactions
|January 3, 2024
PubMed
概括
此摘要是机器生成的。

本研究介绍了一种使用生成对抗网络 (GAN) 创建不确定非线性系统的反线性化控制器 (FLC) 的新方法,提高了跟踪性能和稳定性.

关键词:
凸起式优化的优化反线性化反的线性化生成性的对抗性网络.非线性系统是非线性系统.

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科学领域:

  • 控制系统工程 控制系统工程
  • 人工智能的人工智能
  • 非线性动力学是一种非线性动力学.

背景情况:

  • 不确定的非线性系统带来了重要的控制挑战.
  • 现有的反线性化控制器 (FLC) 与系统不确定性作斗争.
  • 生成对抗网络 (GAN) 提供了学习复杂系统动态的潜力.

研究的目的:

  • 为使用GAN的不确定非线性系统开发一种学习反线性化控制器 (FLC) 的新方法.
  • 为了提高输出输入不确定非线性系统的参考跟踪性能.
  • 为学习控制器的融合和稳定提供理论保证.

主要方法:

  • 使用生成对抗网络 (GAN) 来估计系统的不确定性.
  • 在控制器学习中使用minimax双玩家优化框架.
  • 确保发电机结构严格凸起,并增强对抗损失,以减轻GANs模式崩.
  • 通过全面的模拟和实践实验验证该方法.

主要成果:

  • 拟议的基于GAN的FLC显著提高了不确定的非线性系统中的参考跟踪性能.
  • 建立了强大的FLC对收和稳定的理论保证.
  • 该方法有效地解决了GAN培训中常见的模式崩挑战.
  • 实验结果证实了拟议方法的优越性和有效性.

结论:

  • 生成性对抗性网络提供了一个强大的框架,用于学习强大的反对不确定非线性系统的线性化控制器.
  • 开发的方法提供了增强的性能和稳定性保证,克服了传统方法的局限性.
  • 这项工作表明了在先进的控制系统设计中应用先进的人工智能技术的有希望的方向.