Jove
Visualize
联系我们
JoVE
x logofacebook logolinkedin logoyoutube logo
关于 JoVE
概览领导团队博客JoVE 帮助中心
作者
出版流程编辑委员会范围与政策同行评审常见问题投稿
图书馆员
用户评价订阅访问资源图书馆顾问委员会常见问题
研究
JoVE JournalMethods CollectionsJoVE Encyclopedia of Experiments存档
教育
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab Manual教师资源中心教师网站
使用条款与条件
隐私政策
政策

相关概念视频

Feedback control systems01:26

Feedback control systems

332
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...
332
Linear Approximation in Frequency Domain01:26

Linear Approximation in Frequency Domain

106
Linear systems are characterized by two main properties: superposition and homogeneity. Superposition allows the response to multiple inputs to be the sum of the responses to each individual input. Homogeneity ensures that scaling an input by a scalar results in the response being scaled by the same scalar.
In contrast, nonlinear systems do not inherently possess these properties. However, for small deviations around an operating point, a nonlinear system can often be approximated as linear....
106
Open and closed-loop control systems01:17

Open and closed-loop control systems

785
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...
785
Transfer Function in Control Systems01:21

Transfer Function in Control Systems

548
The transfer function is a fundamental concept in the analysis and design of linear time-invariant (LTI) systems. It offers a concise way to understand how a system responds to different inputs in the frequency domain. It serves as a bridge between the time-domain differential equations that describe system dynamics and the frequency-domain representation that facilitates easier manipulation and analysis.
To derive the transfer function, consider a general nth-order linear time-invariant...
548
Linear Approximation in Time Domain01:21

Linear Approximation in Time Domain

96
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,...
96
Control Systems01:10

Control Systems

1.2K
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...
1.2K

您也可能阅读

相关文章

通过共同作者、期刊和引用图与本文相关的文章。

排序
Same author

Synergetic Learning Neuro-Control for Unknown Affine Nonlinear Systems With Asymptotic Stability Guarantees.

IEEE transactions on neural networks and learning systems·2024
Same author

Efficacy of add-on blonanserin in treatment-resistant schizophrenia therapy: A retrospective cohort study.

Asian journal of psychiatry·2023
Same author

Multiple therapies relieve long-term tardive dyskinesia in a patient with chronic schizophrenia: A case report.

World journal of clinical cases·2023
Same author

Internet appointment has more advantages than traditional appointment in the nursing service of dry eye patients.

Medicine·2023
Same author

Dynamic assessment of dust hazard risk in the reconstruction of old industrial buildings: coupling effects of dust distribution and personnel trajectories.

Environmental science and pollution research international·2023
Same author

Performance evaluation of PLT-H (hybrid-channel platelet) under various interferences and application studies for platelet transfusion decisions.

Platelets·2023
Same journal

TraNce: Type-aware hypergraph neural network with biological mediators for drug repositioning.

Neural networks : the official journal of the International Neural Network Society·2026
Same journal

Decentralized ADMM for factorization-based Low-rank matrix estimation.

Neural networks : the official journal of the International Neural Network Society·2026
Same journal

Memristive neuromorphic circuit design inspired by the neural mechanisms of conditioned fear.

Neural networks : the official journal of the International Neural Network Society·2026
Same journal

Q-learning based asynchronous Boolean control networks stabilization with data loss.

Neural networks : the official journal of the International Neural Network Society·2026
Same journal

New results on prescribed-time synchronization of complex networks via intermittent control.

Neural networks : the official journal of the International Neural Network Society·2026
Same journal

Variance-constrained multi-view ensemble broad network for imbalanced data.

Neural networks : the official journal of the International Neural Network Society·2026
查看所有相关文章

相关实验视频

Updated: Jul 15, 2025

An Experimental Platform to Study the Closed-loop Performance of Brain-machine Interfaces
10:51

An Experimental Platform to Study the Closed-loop Performance of Brain-machine Interfaces

Published on: March 10, 2011

13.8K

使用神经网络对未知非线性H∞控制进行协同学习.

Liao Zhu1, Ping Guo1, Qinglai Wei2

  • 1International Academic Center of Complex Systems, Beijing Normal University, Zhuhai, 519087, Guangdong, China; School of Systems Science, Beijing Normal University, Beijing, 100875, China.

Neural networks : the official journal of the International Neural Network Society
|September 29, 2023
PubMed
概括
此摘要是机器生成的。

本研究引入了一种新的数据驱动的H-无限控制方法,用于未知非线性系统的协同学习. 该算法通过无模型的汉密尔顿 - 雅各比 - 艾萨克斯方程学习最佳策略来实现实时,强大的控制.

关键词:
适应性动态编程是适应性的.数据驱动的数据驱动.H ((∞) 控制控制的时间.神经网络的神经网络非线性系统是非线性系统.时间差异是时间差异.

更多相关视频

Closed-loop Neuro-robotic Experiments to Test Computational Properties of Neuronal Networks
11:18

Closed-loop Neuro-robotic Experiments to Test Computational Properties of Neuronal Networks

Published on: March 2, 2015

10.3K
Real-Time Proxy-Control of Re-Parameterized Peripheral Signals using a Close-Loop Interface
11:54

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

Published on: May 8, 2021

4.4K

相关实验视频

Last Updated: Jul 15, 2025

An Experimental Platform to Study the Closed-loop Performance of Brain-machine Interfaces
10:51

An Experimental Platform to Study the Closed-loop Performance of Brain-machine Interfaces

Published on: March 10, 2011

13.8K
Closed-loop Neuro-robotic Experiments to Test Computational Properties of Neuronal Networks
11:18

Closed-loop Neuro-robotic Experiments to Test Computational Properties of Neuronal Networks

Published on: March 2, 2015

10.3K
Real-Time Proxy-Control of Re-Parameterized Peripheral Signals using a Close-Loop Interface
11:54

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

Published on: May 8, 2021

4.4K

科学领域:

  • 控制理论 控制理论
  • 机器学习 机器学习
  • 非线性系统是非线性系统.

背景情况:

  • H无限度控制提供了稳健性,但与未知的非线性系统作斗争.
  • 现有的方法缺乏对复杂,不可预测的环境的实时适应.

研究的目的:

  • 开发一个在线,实时的协同学习算法,用于数据驱动的H-infinity控制.
  • 在完全未知的同源非线性系统中解决稳定性挑战.

主要方法:

  • 制定了H-无限控制作为一个两人零和游戏.
  • 使用非政策的强化学习推导出一种无模型的汉密尔顿 - 雅各比 - 艾萨克斯方程 (MF-HJIE).
  • 雇佣时间差异学习和经验重复用于在线优化.

主要成果:

  • 证明了MF-HJIE和传统HJIE之间的等价性.
  • 证明了协同学习系统的统一终极局限性.
  • 通过F16飞机和非线性系统的模拟验证了该方法的可操作性.

结论:

  • 拟议的协同学习算法使未知的非线性系统能够有效地以数据为导向的H无限控制.
  • 该方法提供实时,强大的控制解决方案,可适应动态环境.
  • 模拟结果证实了该方法的实际适用性和性能.