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相关概念视频

Feedback control systems01:26

Feedback control systems

295
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...
295
Effects of feedback01:24

Effects of feedback

527
Feedback in control systems plays a critical role in shaping various operational parameters, extending beyond simple error reduction to influence stability, bandwidth, gain, impedance, and sensitivity. Understanding these effects requires examining a basic feedback system characterized by defined input, output, error, and feedback signals.
Feedback significantly modifies the gain of a control system. The gain of a system without feedback is altered by a factor of one plus GH, where G represents...
527
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
Open and closed-loop control systems01:17

Open and closed-loop control systems

678
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...
678
PD Controller: Design01:26

PD Controller: Design

199
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,...
199
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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Closed-loop Neuro-robotic Experiments to Test Computational Properties of Neuronal Networks
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神经网络自适应代学习控制用于严格反未知延迟系统对输入和.

Mouquan Shen, Zihao Wang, Song Zhu

    IEEE transactions on neural networks and learning systems
    |September 25, 2024
    PubMed
    概括

    本研究介绍了神经网络自适应代学习控制 (ILC) 对于具有未知延迟和输入和的非线性系统. 拟议的方法使用Lyapunov-Krasovskii函数和复合能量函数来确保系统的融合.

    科学领域:

    • 控制工程 控制工程 控制工程
    • 非线性系统理论 非线性系统理论
    • 人工智能在控制中

    背景情况:

    • 严格反的非线性系统往往表现出未知的状态延迟和输入和,这给控制带来了重大挑战.
    • 传统的控制方法在这些复杂系统特征的同时存在方面存在困难.
    • 代学习控制 (ILC) 为提高系统在重复任务中的性能提供了一个框架.

    研究的目的:

    • 开发一种新的神经网络自适应代学习控制 (ILC) 策略.
    • 在严格反非线性系统中有效解决未知状态延迟和输入和.
    • 确保控制系统的趋同和稳定.

    主要方法:

    • 构建Lyapunov-Krasovskii (L-K) 功能,用于处理未知状态延迟的子系统.
    • 在虚拟控制器设计过程中使用命令过器来减轻衍生爆炸.
    • 将辅助系统集成到后退框架中,以弥补输入和和过器的缺陷.
    • 超标触角函数和辐射基函数神经网络 (RBF NNs) 的应用用于奇点和未知项近似.

    主要成果:

    • 在严格反非线性系统中成功补偿未知状态延迟和输入和.
    • 通过使用命令过器和辅助系统,证明了避免衍生爆炸.

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  • 闭环系统状态的保证趋同使用复合能量函数 (CEF) 方法.
  • 通过模拟示例验证拟议的算法的有效性.
  • 结论:

    • 拟议的神经网络适应性ILC策略对于控制具有未知状态延迟和输入和度的严格反非线性系统是有效的.
    • L-K 函数,命令过器,辅助系统和 RBF NNs 的组合提供了一个强大的解决方案.
    • 该研究通过模拟证实了理论上的收特性,证实了算法的实际应用.