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相关概念视频

Feedback control systems01:26

Feedback control systems

319
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...
319
State Space Representation01:27

State Space Representation

213
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...
213
Time-Domain Interpretation of PD Control01:07

Time-Domain Interpretation of PD Control

120
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...
120
Open and closed-loop control systems01:17

Open and closed-loop control systems

767
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...
767
Sampling Continuous Time Signal01:11

Sampling Continuous Time Signal

255
In signal processing, a continuous-time signal can be sampled using an impulse-train sampling technique, followed by the zero-order hold method. Impulse-train sampling involves the use of a periodic impulse train, which consists of a series of delta functions spaced at regular intervals determined by the sampling period. When a continuous-time signal is multiplied by this impulse train, it generates impulses with amplitudes corresponding to the signal's values at the sampling points.
In the...
255
Node Analysis for AC Circuits01:14

Node Analysis for AC Circuits

327
Consider an angioplasty system featuring a catheter equipped with a turbine, a critical tool for removing plaque deposits from coronary arteries. This intricate medical device operates using a circuit model reminiscent of a dual-node RLC circuit powered by a current-controlled voltage source.
To unravel the complexities of this system, nodal analysis is employed, a powerful technique founded on Kirchhoff's current law (KCL), which remains valid for phasors. AC circuits can effectively be...
327

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相关实验视频

Updated: Jul 11, 2025

WheelCon: A Wheel Control-Based Gaming Platform for Studying Human Sensorimotor Control
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数据驱动的NODE基于多级采样数据状态反控制.

Long Zhao1, Shihua Li1, Rongjie Liu2

  • 1School of Automation Southeast University, Nanjing, China.

ISA transactions
|November 10, 2023
PubMed
概括
此摘要是机器生成的。

本研究引入了一种新的方法,用于稳定多速率采样数据系统,使用神经普通微分方程 (NODE). 它提供了选择多个样本周期的标准,以确保系统稳定性和优化控制器参数.

关键词:
数据驱动的数据驱动.多级抽样数据的多级抽样数据.神经常规微分方程 神经常规微分方程非线性系统是非线性系统.优化优化 优化优化

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相关实验视频

Last Updated: Jul 11, 2025

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科学领域:

  • 控制系统工程 控制系统工程
  • 非线性系统动态 非线性系统动态
  • 机器人技术 机器人技术 机器人技术

背景情况:

  • 多级采样数据系统提高了控制系统的性能和适应性,特别是混合信号类型.
  • 这些系统的现有控制器可能过于复杂,并且缺乏关于采样间隔选择的明确指导.
  • 在非线性多速率系统中确保稳定性和优化性能仍然是重大挑战.

研究的目的:

  • 为多速率采样数据系统开发一个强大的稳定性标准.
  • 为了优化控制器参数以提高系统性能.
  • 解决非线性多速率系统中现有控制器的局限性.

主要方法:

  • 一种新的方法,结合了系统稳定性和选择多个样本周期的实际考虑.
  • 使用神经常规微分方程 (NODE) 在线性实用控制器中进行最佳参数选择.
  • 设计和实施线性稳定器,使用三种采样级别的多速率采样数据.

主要成果:

  • 建立了一个选择多个样本周期的标准,以保证系统稳定性.
  • 使用NODE确定了最佳控制器参数,根据预定义的索引最大限度地提高性能.
  • 稳定性和控制器设计在三个不同的采样水平上成功分析.

结论:

  • 拟议的战略有效地保证了非线性多速率采样数据系统中的系统稳定性.
  • 该方法提供了控制器参数选择的最佳方法,提高了系统性能.
  • 通过模拟和现实世界单链机器人系统应用来证明有效性.