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相关概念视频

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,...
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State Space Representation01:27

State Space Representation

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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...
210
Transfer Function to State Space01:23

Transfer Function to State Space

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State-space representation is a powerful tool for simulating physical systems on digital computers, necessitating the conversion of the transfer function into state-space form. Consider an nth-order linear differential equation with constant coefficients, like those encountered in an RLC circuit. The state variables are selected as the output and its n−1 derivatives. Differentiating these variables and substituting them back into the original equation produces the state equations.
In an...
262
State Space to Transfer Function01:21

State Space to Transfer Function

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The conversion of state-space representation to a transfer function is a fundamental process in system analysis. It provides a method for transitioning from a time-domain description to a frequency-domain representation, which is crucial for simplifying the analysis and design of control systems.
The transformation process begins with the state-space representation, characterized by the state equation and the output equation. These equations are typically represented as:
212
Residuals and Least-Squares Property01:11

Residuals and Least-Squares Property

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The vertical distance between the actual value of y and the estimated value of y. In other words, it measures the vertical distance between the actual data point and the predicted point on the line
If the observed data point lies above the line, the residual is positive, and the line underestimates the actual data value for y. If the observed data point lies below the line, the residual is negative, and the line overestimates the actual data value for y.
The process of fitting the best-fit...
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Linear Approximation in Frequency Domain01:26

Linear Approximation in Frequency Domain

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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....
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Fault detection and identification in induction motor using weightless neural network.

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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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使用扩展状态空间递归最小平方的非线性轨迹估计.

Anam Abid1

  • 1Department of Mechatronics Engineering, University of Engineering and Technology, Peshawar, Pakistan.

The Review of scientific instruments
|November 27, 2023
PubMed
概括
此摘要是机器生成的。

本研究引入了扩展状态空间递归最小平方 (ESSRLS) 过器,以改进非线性轨迹估计. 与现有方法相比,ESSRLS过器在具有挑战性的条件下提供了更高的性能.

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科学领域:

  • 控制系统工程 控制系统工程
  • 信号处理 信号处理
  • 机器人和自动化 机器人和自动化

背景情况:

  • 非线性系统模型在目标跟踪,GPS和自主机器人等应用程序的估计任务中带来了重大挑战.
  • 像扩展卡尔曼波器 (EKF) 和无气味卡尔曼波器 (UKF) 这样的现有波器在处理复杂的非线性和未知噪声统计数据方面存在局限性.

研究的目的:

  • 导出和呈现一个扩展状态空间递归最小方程 (ESSRLS) 过器,专门用于非自主系统中的非线性轨迹估计.
  • 评估拟议的ESSRLS过器的性能,并将其与EKF和UKF等既有方法进行比较.

主要方法:

  • 对非自主系统的ESSRLS过算法的导出.
  • 使用机动飞机轨迹估计模拟的比较性能分析.
  • 在模型不确定性,数据中断 (封闭) 和较大的初始条件偏差条件下进行评估.

主要成果:

  • 与EKF和UKF相比,ESSRLS波器表现出优越的估计性能.
  • 拟议的波器独立于先验的噪声统计数据,使用可调节的忘记因子.
  • 即使有显著的模型不确定性,数据丢失和初始条件错误,也显示出有效的性能.

结论:

  • ESSRLS波器是用于非线性轨迹估计的强大而实用的解决方案,性能优于当前最先进的波器.
  • 它与噪声统计和可调节参数的独立性使其非常适合用于现实世界的非线性过应用.
  • 在充满挑战的动态环境中,ESSRLS过器提供了更高的准确性和可靠性.