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相关概念视频

Feedback control systems01:26

Feedback control systems

252
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...
252
Linear Approximation in Time Domain01:21

Linear Approximation in Time Domain

53
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.
For a simple pendulum with a mass evenly distributed along its length and the center of mass located at half the pendulum's length,...
53
PD Controller: Design01:26

PD Controller: Design

140
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,...
140
Time-Domain Interpretation of PD Control01:07

Time-Domain Interpretation of PD Control

66
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...
66
State Space Representation01:27

State Space Representation

145
The frequency-domain technique, commonly used in analyzing and designing feedback control systems, is effective for linear, time-invariant systems. However, it falls short when dealing with nonlinear, time-varying, and multiple-input multiple-output systems. The time-domain or state-space approach addresses these limitations by utilizing state variables to construct simultaneous, first-order differential equations, known as state equations, for an nth-order system.
Consider an RLC circuit, a...
145
Open and closed-loop control systems01:17

Open and closed-loop control systems

562
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...
562

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相关实验视频

Updated: May 9, 2025

WheelCon: A Wheel Control-Based Gaming Platform for Studying Human Sensorimotor Control
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部分未知动态的采样数据系统的基于学习的MPC.

Seungyong Han1, Xuyang Guo2, Suneel Kumar Kommuri3

  • 1Department of Mechanical System Engineering, Jeonbuk National University, 567, Baekje-daero, Deokjin-gu, Jeonju 54896, Republic of Korea.

ISA transactions
|May 3, 2025
PubMed
概括
此摘要是机器生成的。

本研究介绍了一种新的基于学习的模型预测控制 (LMPC),用于具有未知动态的系统. 该方法使用神经普通微分方程和Gronwall-Bellman不等式来控制采样数据,确保了系统的稳定性.

关键词:
基于学习的模型预测控制预测控制.神经常规微分方程 神经常规微分方程采样数据控制系统的控制系统最终的有限性.

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相关实验视频

Last Updated: May 9, 2025

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科学领域:

  • 控制系统工程 控制系统工程
  • 机器学习 机器学习
  • 动态系统 动态系统

背景情况:

  • 现实世界的控制系统通常具有时间变化的参数和不规则的数据采样,使准确的建模和稳定性保证变得复杂.
  • 现有的模型预测控制 (MPC) 方法与这些不确定性作斗争,限制了它们的适用性.
  • 准确的系统识别和强大的控制对于可靠的操作至关重要.

研究的目的:

  • 为部分未知动态的采样数据控制系统提出一种基于学习的新型模型预测控制 (LMPC) 方法.
  • 为了应对时间变化的参数和不规则的数据采样所带来的挑战.
  • 在不确定的环境中确保系统稳定性和局限性.

主要方法:

  • 通过神经常规微分方程 (NODE) 来从不规则地采集的数据中学习未知的时间变化动态.
  • 学习动态模型被集成到采样数据的MPC框架中.
  • 格伦沃尔-贝尔曼不等式被用来推导出保证最终局限性的条件.

主要成果:

  • 拟议的LMPC方法有效地学习和适应未知的系统动态.
  • 定量稳定性分析证实了系统的最终局限性.
  • 通过两个实践示例来证明该方法的适用性.

结论:

  • 开发的LMPC为控制部分未知和时间变化的动态系统提供了强大的解决方案.
  • NODE和MPC的整合为处理不规则采样提供了一个强大的框架.
  • 该方法在现实应用中提高了采样数据控制系统的可靠性和稳定性.