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相关概念视频

One-Degree-of-Freedom System01:24

One-Degree-of-Freedom System

555
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...
555
Relative Motion Analysis using Rotating Axes-Problem Solving01:29

Relative Motion Analysis using Rotating Axes-Problem Solving

448
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...
448
Feedback control systems01:26

Feedback control systems

416
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...
416
Open and closed-loop control systems01:17

Open and closed-loop control systems

987
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...
987
Relative Motion Analysis using Rotating Axes01:25

Relative Motion Analysis using Rotating Axes

530
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...
530
Three-Dimensional Force System:Problem Solving01:30

Three-Dimensional Force System:Problem Solving

853
A three-dimensional force system refers to a scenario in which three forces act simultaneously in three different directions. This type of problem is commonly encountered in physics and engineering, where it is necessary to calculate the resultant force on the system, which can then be used to predict or analyze the behavior of the object or structure under consideration.
To solve a three-dimensional force system, first resolve each force into its respective scalar components. Do this using...
853

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相关实验视频

Updated: Sep 9, 2025

Design and Application of a Fault Detection Method Based on Adaptive Filters and Rotational Speed Estimation for an Electro-Hydrostatic Actuator
06:45

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基于辐射基函数的神经网络近似的快速非单元自适应超扭转滑动模式控制

Xiao Lin1, Junyang Li1, Yankui Song2

  • 1State Key Laboratory of Mechanical Transmissions for Advanced Equipment, Chongqing University, Chongqing 400030, China.

ISA transactions
|August 29, 2025
PubMed
概括

这项研究引入了基于神经网络的机器人关节的新型自适应控制,提高了精度和干扰排斥. 改进的快速非单元超扭转控制确保了复杂的机器人应用中的强大性能.

关键词:
适应性增益没有单数辐射基础功能神经网络机器人联合模块超扭转的滑动模式控制

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科学领域:

  • 机器人技术
  • 控制系统工程
  • 人工智能

背景情况:

  • 机器人关节模块需要精确控制复杂的任务.
  • 在机器人控制中,非线性摩擦和刚性存在重大挑战.
  • 现有的控制方案往往与奇点和外部干扰作斗争.

研究的目的:

  • 为机器人关节模块开发一个改进的快速非单元自适应超扭转控制方案.
  • 为了提高轨迹跟踪精度和干扰排斥能力.
  • 用基于神经网络的补偿来解决机器人系统的精确控制问题.

主要方法:

  • 使用拉格朗的能量方程建立了机器人关节模块的二次状态空间模型.
  • 提议改进一个快速的非单点终端滑动表面,以避免单点并加速融合.
  • 为不确定模型因子设计了一个辐射基础功能神经网络补偿器和一个自适应切换控制定律.

主要成果:

  • 拟议的控制方案在各种参考轨迹下显示出卓越的轨迹跟踪性能.
  • 在存在外部干扰的情况下显示出有效的干扰排斥能力.
  • 模拟和实验结果验证了控制策略的有效性和稳定性.

结论:

  • 基于神经网络的新型自适应超扭转控制方案显著提高了机器人关节模块的精确控制.
  • 该方法提供了针对模型不确定性和外部干扰的强大稳定性.
  • 由于无精确信息的适应性干扰排斥,工程的实际适用性得到改善.