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相关概念视频

Open and closed-loop control systems01:17

Open and closed-loop control systems

1.1K
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...
1.1K
Control System Problem01:21

Control System Problem

178
In an open-loop system, such as a basic thermostat, the poles of the transfer function influence the system's response but do not determine its stability. However, when feedback is introduced to form a closed-loop system, such as an advanced thermostat that adjusts heating based on room temperature, stability is governed by the new poles of the closed-loop transfer function.
When forming a closed-loop system, issues can arise if the poles cross into the unstable region, leading to potential...
178
Distributed Loads: Problem Solving01:21

Distributed Loads: Problem Solving

745
Beams are structural elements commonly employed in engineering applications requiring different load-carrying capacities. The first step in analyzing a beam under a distributed load is to simplify the problem by dividing the load into smaller regions, which allows one to consider each region separately and calculate the magnitude of the equivalent resultant load acting on each portion of the beam. The magnitude of the equivalent resultant load for each region can be determined by calculating...
745
Feedback control systems01:26

Feedback control systems

442
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...
442
Multimachine Stability01:25

Multimachine Stability

237
Multimachine stability analysis is crucial for understanding the dynamics and stability of power systems with multiple synchronous machines. The objective is to solve the swing equations for a network of M machines connected to an N-bus power system.
In analyzing the system, the nodal equations represent the relationship between bus voltages, machine voltages, and machine currents. The nodal equation is given by:
237
Control Systems: Applications01:25

Control Systems: Applications

748
Electrical engineering plays a pivotal role in our daily lives, with control systems at the heart of many applications, from home appliances to sophisticated space shuttles. Control systems manage and regulate the behavior of devices and processes, ensuring they function safely, correctly, and efficiently.
In modern vehicles, control systems manage various functions to enhance performance and safety. The steering wheel and accelerator are primary inputs in a car's control system. The...
748

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相关实验视频

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Real-Time Proxy-Control of Re-Parameterized Peripheral Signals using a Close-Loop Interface
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Real-Time Proxy-Control of Re-Parameterized Peripheral Signals using a Close-Loop Interface

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基于自适应神经网络的异步控制,用于切换未知死区的网络物理系统.

Jun Cheng, Junhui Wu, Huaicheng Yan

    IEEE transactions on cybernetics
    |May 26, 2025
    PubMed
    概括

    本研究介绍了适应性神经网络控制,用于切换未知的死区的网络物理系统. 该方法确保了系统稳定性,尽管异步切换和未知的输入,通过模拟验证.

    科学领域:

    • 控制系统工程 控制系统工程
    • 网络物理系统 网络物理系统
    • 人工智能的人工智能

    背景情况:

    • 交换网络物理系统 (CPS) 由于异步交换和未知的系统参数而存在控制挑战.
    • 现有的方法通常依赖于马尔科夫/半马尔科夫过程,这可能是计算密集的.
    • 网络环境中的未知死区进一步复杂化了CPS的控制设计.

    研究的目的:

    • 开发一种适应性神经网络控制策略,用于切换未知死区的CPS.
    • 使用通用切换规则分析系统行为,减少计算负载.
    • 在不确定的条件下确保闭环系统的稳定性和局限性.

    主要方法:

    • 利用一个通用的切换规则来建模子系统切换动态.
    • 采用自适应神经网络来控制法律设计,以处理未知的死区.
    • 开发了一个动态调整的基于和的观察器,以减轻不可预见的信息效应.
    • 在稳定性分析中应用了利亚普诺夫函数方法,确保了概率的边界性.

    主要成果:

    • 建立了足够的标准来确保闭环系统在概率上保持有限.
    • 证明了自适应神经网络控制法在管理未知的死区方面的有效性.

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  • 通过动态调整其和水平来展示基于和的观察者的灵活性.
  • 结论:

    • 拟议的自适应神经网络异步控制方法对于切换未知死区的CPS是有效的.
    • 一般化切换规则和观察者设计在计算负载和灵活性方面提供了实际优势.
    • 模拟结果验证了开发的控制策略的稳定性和实用性.