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相关概念视频

Time-Domain Interpretation of PD Control01:07

Time-Domain Interpretation of PD Control

180
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...
180
PD Controller: Design01:26

PD Controller: Design

353
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,...
353
PI Controller: Design01:24

PI Controller: Design

503
Proportional Integral (PI) controllers are a fundamental component in modern control systems, widely used to enhance performance and mitigate steady-state errors. They are particularly effective in applications such as automatic brightness adjustment on smartphones, where they excel at mitigating steady-state errors for step-function inputs. Unlike PD controllers, which require time-varying errors to function optimally, PI controllers leverage their integral component to address residual...
503
Transmission Shafts: Problem Solving01:09

Transmission Shafts: Problem Solving

291
Designing a solid shaft that transmits power from a motor to a machine tool involves a series of calculations to ensure the shaft can withstand the stresses applied by bending moments and torques. First, calculate the torque exerted on the gear, considering the power transmitted by the shaft and its rotational speed. Following this, compute the tangential forces acting on the gears, which directly relate to the torque and the gear radius.
Next, use bending moment diagrams for the shaft to...
291
Multimachine Stability01:25

Multimachine Stability

230
Multimachine stability analysis is crucial for understanding the dynamics and stability of power systems with multiple synchronous machines. The objective is to solve the swing equations for a network of M machines connected to an N-bus power system.
In analyzing the system, the nodal equations represent the relationship between bus voltages, machine voltages, and machine currents. The nodal equation is given by:
230
Load-frequency control01:28

Load-frequency control

260
Load-frequency control (LFC) is vital for maintaining power system stability, ensuring that frequency and power flows remain within acceptable limits during load changes. Turbine-governor control eliminates rotor accelerations and decelerations following load changes. However, a steady-state frequency error persists when the change in the turbine-governor reference setting is zero. In an interconnected power system, each area agrees to export or import a scheduled amount of power through...
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相关实验视频

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An Experimental Platform to Study the Closed-loop Performance of Brain-machine Interfaces
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基于CEEMDAN-TPE-LightGBM-APC算法的直驱转盘的动态错误建模和预测补偿.

Manzhi Yang1, Hao Ren1, Shijia Liu1

  • 1College of Mechanical Engineering, Xi'an University of Science and Technology, No. 58 Yanta Middle Road, Xi'an 710054, China.

Micromachines
|July 30, 2025
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概括

这项研究引入了一种用于直驱转盘的新型动态错误补偿模型,通过精密机械系统的先进分解和机器学习预测技术显著提高定位准确性.

关键词:
轻GBMM 轻GBM 轻GBM 轻GBM适应性纠正适应性的纠正有直接驱动的转盘.定位错误是因为定位错误.预测性补偿的预测性补偿

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科学领域:

  • 机械工程 机械工程
  • 控制系统工程 控制系统工程
  • 信号处理 信号处理

背景情况:

  • 直接驱动的转盘对于高精度的宏微驱动系统至关重要.
  • 这些转盘的定位精度对于整体系统性能至关重要.
  • 准确的错误预测和补偿对于先进的控制策略至关重要.

研究的目的:

  • 为直接驱动的转盘开发一个动态的连续错误补偿模型.
  • 为了提高直驱转盘的定位准确度.
  • 为了能够在线预测和纠正定位错误.

主要方法:

  • 采用了"分解-建模-整合-纠正"的策略.
  • 用自适应噪声 (CEEMDAN) 进行完整的综合实证模式分解分解了历史错误数据.
  • 树结构帕森估计器 (TPE) 优化的光梯度增强机 (LightGBM) 模型预测了组件错误,随后进行了自适应预测校正 (APC).

主要成果:

  • 补偿定位错误范围在测试和推断测试组中显著减少.
  • 定位错误的标准偏差减少了71.2% (测试组) 和61.6% (外推测试组).
  • 该模型表现出高度的灵活性,适应性和在线预测-校正能力.

结论:

  • 提出的动态连续错误补偿模型有效地提高了直驱转盘的准确性.
  • 该方法保持了预测稳定性和运营效率.
  • 这项研究对精密机械系统的错误补偿具有重要的理论和实践价值.