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Updated: Jun 29, 2025

The Modular Design and Production of an Intelligent Robot Based on a Closed-Loop Control Strategy
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HiDeS:多机器人系统和意见动态的高阶导数监督的神经普通微分方程

Meng Li1,2, Wenyu Bian1, Liangxiong Chen1

  • 1Zhangjiajie College, Zhangjiajie, China.

Frontiers in neurorobotics
|March 28, 2024
PubMed
概括

本研究介绍了高阶导数监督 (HiDeS) 神经常规微分方程 (NODEs),以改进复杂动态的建模. HiDeS NODE通过使用更高阶衍生品作为监督信号来提高预测准确性.

关键词:
多机器人系统是多机器人系统.神经网络的神经网络的神经网络神经常规微分方程 神经常规微分方程意见的动态 意见的动态机器人技术 机器人工程 机器人工程

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Second Order systems I

A servo system exemplifies a second-order system, featuring a proportional controller and load elements that ensure the output position aligns with the input position. The relationship between these components is described by a second-order differential equation. Applying the Laplace transform under zero initial conditions yields the transfer function, showing how inputs are converted to outputs in the system.
By reinterpreting the system, one can derive the closed-loop transfer function, which...
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PD Controller: Design01:26

PD Controller: Design

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.
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Second Order systems II01:18

Second Order systems II

In an underdamped second-order system, where the damping ratio ζ is between 0 and 1, a unit-step input results in a transfer function that, when transformed using the inverse Laplace method, reveals the output response. The output exhibits a damped sinusoidal oscillation, and the difference between the input and output is termed the error signal. This error signal also demonstrates damped oscillatory behavior. Eventually, as the system reaches a steady state, the error diminishes to zero.
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One-Degree-of-Freedom System01:24

One-Degree-of-Freedom System

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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.
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