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相关概念视频

Feedback control systems01:26

Feedback control systems

296
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...
296
Control Systems01:10

Control Systems

1.1K
Control systems are everywhere in contemporary society, influencing diverse applications from aerospace to automated manufacturing. These systems can be found naturally within biological processes, such as blood sugar regulation and heart rate adjustment in response to stress, as well as in man-made systems like elevators and automated vehicles. A control system is essentially a network of subsystems and processes that collaboratively convert specific inputs into desired outputs.
At the heart...
1.1K
Linear Approximation in Time Domain01:21

Linear Approximation in Time Domain

72
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,...
72
Control System Problem01:21

Control System Problem

110
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...
110
Open and closed-loop control systems01:17

Open and closed-loop control systems

685
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...
685
Time-Domain Interpretation of PD Control01:07

Time-Domain Interpretation of PD Control

85
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...
85

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对非线性系统使用强化学习策略的基于观察者的最佳后退安全控制.

Qinglai Wei, Wendi Chen, Xiangmin Tan

    IEEE transactions on cybernetics
    |September 4, 2024
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    概括
    此摘要是机器生成的。

    本研究介绍了一种新的强化学习 (RL) 策略,用于在非线性系统中基于观察者的最佳控制,有效抵御欺骗攻击. 新方法通过使用神经网络和先进的观察者设计来提高系统安全性和性能.

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    科学领域:

    • 控制系统工程 控制系统工程
    • 人工智能的人工智能
    • 网络安全 网络安全

    背景情况:

    • 非线性系统容易受到欺骗攻击,损害控制系统的完整性.
    • 现有的最佳控制策略通常依赖于关于欺骗攻击的严格假设,限制了它们的适用性.
    • 强化学习 (RL) 为适应性控制提供了一种有前途的方法,但需要对抗逆境条件的强大设计.

    研究的目的:

    • 开发基于观察者的最佳后退安全控制,用于对欺骗攻击有弹性的非线性系统.
    • 引入一个改进的安全RL算法,利用神经网络来增强抗攻击性和系统优化.
    • 通过设计一个观察者来放松关于欺骗攻击的常见假设,该观察者可以从攻击的输出中估计状态.

    主要方法:

    • 提出了一个以观察员为基础的最佳后退安全控制框架.
    • 使用神经网络技术的基于批评演员的强化学习 (RL) 算法.
    • 一个先进的观察者被设计用于估计系统状态使用攻击输出,减轻欺骗影响.

    主要成果:

    • 拟议的RL算法有效地抵御非线性系统中的欺骗攻击.
    • 开发的观察器成功地估计了不可用状态,消除了攻击的影响.
    • 控制策略确保封闭循环系统中的半全球终极边界信号.

    结论:

    • 基于观察者的最佳后退安全控制策略有效地提高了在欺骗攻击下非线性系统的安全性和性能.
    • 整合RL和神经网络为弹性控制提供了一个强大的框架.
    • 该研究通过模拟实验证明了拟议方法的有效性.