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相关概念视频

Feedback control systems01:26

Feedback control systems

433
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...
433
Open and closed-loop control systems01:17

Open and closed-loop control systems

1.0K
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...
1.0K
Control Systems01:10

Control Systems

1.4K
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...
1.4K
Multi-input and Multi-variable systems01:22

Multi-input and Multi-variable systems

152
Cruise control systems in cars are designed as multi-input systems to maintain a driver's desired speed while compensating for external disturbances such as changes in terrain. The block diagram for a cruise control system typically includes two main inputs: the desired speed set by the driver and any external disturbances, such as the incline of the road. By adjusting the engine throttle, the system maintains the vehicle's speed as close to the desired value as possible.
In the absence...
152
Controller Configurations01:22

Controller Configurations

153
Controller configurations are crucial in a car's cruise control system because they manage speed over time to maintain a consistent pace regardless of road conditions, thereby meeting design goals. In traditional control systems, fixed-configuration design involves predetermined controller placement. System performance modifications are known as compensation.
Control-system compensation involves various configurations, most commonly series or cascade compensation, in which the controller...
153
Classification of Systems-I01:26

Classification of Systems-I

318
Linearity is a system property characterized by a direct input-output relationship, combining homogeneity and additivity.
Homogeneity dictates that if an input x(t) is multiplied by a constant c, the output y(t) is multiplied by the same constant. Mathematically, this is expressed as:
318

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相关实验视频

Updated: Sep 16, 2025

WheelCon: A Wheel Control-Based Gaming Platform for Studying Human Sensorimotor Control
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基于数据的非线性约束互连系统的去中心化控制,使用强化学习.

Guang Yang1, Xiong Yang2

  • 1School of Information Engineering, Zhongnan University of Economics and Law, Wuhan 430073, China.

Neural networks : the official journal of the International Neural Network Society
|July 5, 2025
PubMed
概括
此摘要是机器生成的。

本研究介绍了一种新的数据驱动的分散控制器,用于具有输入限制的复杂非线性系统. 该方法利用强化学习和神经网络来解决最佳控制问题,增强系统的稳定性和性能.

关键词:
适应式动态编程是适应式的.分散式的控制控制去中心化.神经网络的神经网络的神经网络最佳的控制控制是最好的控制.强化学习是一种强化学习.

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科学领域:

  • 控制理论 控制理论
  • 非线性系统是非线性系统.
  • 人工智能的人工智能

背景情况:

  • 设计用于具有不对称输入约束的相互连接非线性系统的控制器具有挑战性.
  • 现有的方法通常需要精确的系统模型,这些模型并不总是可用.
  • 分散控制策略对于电网等大型系统至关重要.

研究的目的:

  • 开发基于数据的去中心化控制器,用于不匹配的相互连接的非线性系统,具有不对称的输入约束.
  • 在没有明确的系统模型的情况下,利用强化学习来解决复杂的最佳控制问题.
  • 用实例来验证控制器的有效性.

主要方法:

  • 在强化学习框架内使用基于数据的政策代 (PI) 算法.
  • 在PI算法中,使用神经网络 (NN) 实现了一个演员-关键结构.
  • 权重残余方法和蒙特卡洛集成被结合起来,以训练演员和批评者NNs.

主要成果:

  • 分散控制器的设计是基于对辅助子系统不受约束输入的最佳控制问题的解决方案.
  • 提出的方法成功地同时确定了演员和评论家NN的重量参数.
  • 该控制器在稳定相互连接的电力系统方面表现出有效性.

结论:

  • 开发的基于数据的去中心化控制器有效地处理不匹配的相互连接的非线性系统,具有不对称的输入约束.
  • 基于强化学习的政策代算法为解决相关的汉密尔顿-雅各比-贝尔曼方程提供了一种可行的方法.
  • 该研究验证了拟议的控制策略在复杂系统中的实际适用性.