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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

395
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...
395
One-Degree-of-Freedom System01:24

One-Degree-of-Freedom System

481
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...
481
Relative Motion Analysis using Rotating Axes01:25

Relative Motion Analysis using Rotating Axes

456
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...
456
Kinematic Equations for Rotation01:30

Kinematic Equations for Rotation

320
In mechanics, when one observes a rigid body in rotational motion with constant angular acceleration, it is possible to establish equations for its rotational kinematics. This process resembles how linear kinematics are dealt with in simpler motion studies.
For instance, imagine a point A on a rigid body engaged in circular motion. The translational velocity of this particular point can be calculated by taking the time derivatives of the displacement equation, which essentially measures the...
320
Relative Motion Analysis using Rotating Axes - Acceleration01:22

Relative Motion Analysis using Rotating Axes - Acceleration

329
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...
329
Absolute Motion Analysis- General Plane Motion01:24

Absolute Motion Analysis- General Plane Motion

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Visualize a drone, with its propellers spinning rapidly, hovering mid-air. The fascinating movements and operations of this drone can be comprehended by applying the principle of general plane motion.
As the drone's propellers rotate, an upward force is generated that counteracts the force of gravity, enabling the drone to lift off from the ground. This initial movement of the drone is along a straight path, representing a form of translational motion. In this phase, every point on the...
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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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一个基于物理的神经网络方法来增强动力学多旋转器的视觉服务.

Archit Krishna Kamath, Sreenatha G Anavatti, Mir Feroskhan

    IEEE transactions on cybernetics
    |July 8, 2024
    PubMed
    概括

    本研究介绍了一种新的视觉服务策略,用于使用物理信息的神经网络 (PINNs) 估计系统不确定性. 这种方法提高了稳定性,并减少了对精确机器人控制的数据需求.

    科学领域:

    • 机器人技术 机器人技术 机器人技术
    • 控制系统 控制系统
    • 机器学习 机器学习

    背景情况:

    • 视觉伺服使机器人能够使用相机反来精确控制运动.
    • 由于复杂的控制输入和潜在的不确定性,多轮机动态带来了挑战.
    • 基于物理学的神经网络 (PINNs) 提供了一个强大的工具,用于模拟具有有限数据的复杂系统.

    研究的目的:

    • 通过将PINN与动态中心控制集成,为多旋转机开发出强大的视觉伺服器战略.
    • 为了消除在多旋转机运动控制中需要反向雅可比式计算的需要.
    • 为了提高视觉伺服器的稳定性,应对摄像头和多旋转器参数的不确定性.

    主要方法:

    • 使用物理信息神经网络 (PINN) 来估计系统的不确定性和不准确性.
    • PINN模型与以动力为中心的视觉伺服技术集成,直接将像素变化映射到扭矩和推力输入.
    • 一个具有适应性视界的非线性模型预测控制器 (NMPC) 用于实时实现.

    主要成果:

    • 与现有的数据驱动方法相比,拟议的方法减少了65%的标记数据的需求.
    • 集成系统在摄像机参数不确定性高达70%的情况下表现出稳健性.
    • 通过NMPC,控制力度的处理速度比传统的MPC策略快10倍.

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    Published on: March 10, 2011

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    Force and Position Control in Humans - The Role of Augmented Feedback
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    Force and Position Control in Humans - The Role of Augmented Feedback

    Published on: June 19, 2016

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    结论:

    • 结合PINN和以动力为中心的视觉伺服策略,为多轮控制提供了强大且数据效率高的解决方案.
    • 这种方法有效地处理系统不确定性和建模不准确性,这对于现实应用至关重要.
    • NMPC的实时功能确保了动态轨迹跟踪的实际实施.