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相关概念视频

Relative Motion Analysis using Rotating Axes-Problem Solving01:29

Relative Motion Analysis using Rotating Axes-Problem Solving

389
Consider a crane whose telescopic boom rotates with an angular velocity of 0.04 rad/s and angular acceleration of 0.02 rad/s2. Along with the rotation, the boom also extends linearly with a uniform speed of 5 m/s. The extension of the boom is measured at point D, which is measured with respect to the fixed point C on the other end of the boom. For the given instant, the distance between points C and D is 60 meters.
Here, in order to determine the magnitude of velocity and acceleration for point...
389
Relative Motion Analysis using Rotating Axes01:25

Relative Motion Analysis using Rotating Axes

448
Consider a component AB undergoing a linear motion. Along with a linear motion, point B also rotates around point A. To comprehend this complex movement, position vectors for both points A and B are established using a stationary reference frame.
However, to express the relative position of point B relative to point A, an additional frame of reference, denoted as x'y', is necessary. This additional frame not only translates but also rotates relative to the fixed frame, making it...
448
One-Degree-of-Freedom System01:24

One-Degree-of-Freedom System

465
In mechanical engineering, one-degree-of-freedom systems form the basis of a wide range of electrical and mechanical components. Using these models, engineers can predict the behavior of various parts in a larger system, which gives them insight into how different forces interact with each other.
A one-degree-of-freedom system is defined by an independent variable that determines its state and behavior. One example of a one-degree-of-freedom system is a simple harmonic oscillator, such as a...
465
Relative Motion Analysis using Rotating Axes - Acceleration01:22

Relative Motion Analysis using Rotating Axes - Acceleration

322
Consider a component AB undergoing a linear motion. Along with a linear motion, point B also rotates around point A. To comprehend this complex movement, position vectors for both points A and B are established using a stationary reference frame. The absolute velocity of point B is determined by adding the absolute velocity of point A, the relative velocity of point B in the rotating frame, and the effects caused by the angular velocity within the rotating frame.
Time differentiation is...
322
Open and closed-loop control systems01:17

Open and closed-loop control systems

660
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...
660
PID Controller01:19

PID Controller

102
Proportional-Integral-Derivative (PID) controllers are widely used in various control systems to enhance stability and performance. In a thermostat, it adjusts heating or cooling based on the temperature difference between the actual and desired levels. They are often used in automotive speed systems, effectively managing sudden speed changes while maintaining a constant speed under varying conditions. On the other hand, PI controllers, commonly employed in voltage regulation, enhance stability...
102

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Operation of the Collaborative Composite Manufacturing CCM System
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使用自适应滑动模式控制的6RSS并行机器人的基于位置的视觉伺服.

Ningyu Zhu1, Wen-Fang Xie1, Henghua Shen1

  • 1Department of Mechanical, Industrial and Aerospace Engineering, Concordia University, Montreal, Quebec H3G 1M8, Canada.

ISA transactions
|November 4, 2024
PubMed
概括

这项研究引入了基于视觉的平行机器人的控制,提高了轨迹跟踪的准确性. 适应式滑动模式控制系统有效地管理复杂的机器人动态和不确定性.

科学领域:

  • 机器人技术 机器人技术 机器人技术
  • 控制系统工程 控制系统工程
  • 计算机视觉 计算机视觉

背景情况:

  • 对平行机器人的轨迹跟踪是复杂的,因为复杂的动力学和动力学.
  • 现有的控制方法往往难以实现实时准确性和适应性.

研究的目的:

  • 开发一个可靠的基于位置的视觉伺服 (PBVS) 控制策略,用于一个6个革命-球形-球形 (6-RSS) 平行机器人.
  • 为了提高在不确定和时间变化的条件下轨迹跟踪性能.

主要方法:

  • 实施了PBVS方法,使用C-Track 780摄像度传感器进行实时端效应器姿势测量.
  • 采用自适应卡尔曼波器,通过减轻噪声来提高视觉测量准确度.
  • 设计了一个具有辐射基函数 (RBF) 神经网络的自适应滑动模式控制器,用于自动调节控制收益.

主要成果:

  • 拟议的控制器证明了6RSS并行机器人的有效轨迹跟踪.
  • 适应式卡尔曼波器提高了姿势估计的准确性.
  • 基于RBF的自适应滑动模式控制显示了对系统不确定性和时间变化的条件的稳定性.

结论:

关键词:
适应滑动模式控制自适应式滑动模式控制平行机器人是一个平行机器人.基于位置的视觉服务器.在RBF神经网络中.

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  • 开发的带有自适应滑动模式控制的PBVS策略为并行机器人轨迹跟踪提供了卓越的解决方案.
  • 实验验证证了拟议的控制系统的有效性和稳定性.