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相关概念视频

Feedback control systems01:26

Feedback control systems

277
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...
277
Control Systems01:10

Control Systems

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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...
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Neural Circuits01:25

Neural Circuits

1.0K
Neural circuits and neuronal pools are two of the main structures found in the nervous system. Neural circuits are networks of neurons that work together to carry out a specific task or process. They consist of interconnected neurons and glial cells, which provide structural and metabolic support.
Neuronal pools are collections of nerve cells with similar functions and interact through chemical and electrical signals. These pools include both interneurons (the central neural circuit nodes that...
1.0K
Hierarchy of Motor Control01:18

Hierarchy of Motor Control

2.4K
The hierarchy of motor control refers to the different levels of organization and processing involved in controlling movement in the body. These levels range from higher cortical areas involved in planning and decision-making to lower spinal cord reflexes that respond automatically to external stimuli.
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Control Systems: Applications01:25

Control Systems: Applications

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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...
568
Multi-input and Multi-variable systems01:22

Multi-input and Multi-variable systems

94
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...
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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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预定义时间的自适应神经网络,用于大规模互连系统的去中心化控制,具有输入歇斯底里.

Xiaoli Li1, Guoju Zhang2, Yingshan Zhou2

  • 1School of Information Science and Technology, Beijing University of Technology, Beijing 100124, China; Beijing Key Laboratory of Computational Intelligence and Intelligent System, Beijing University of Technology, Beijing 100124, China.

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概括

这项研究引入了一种新的适应神经网络控制器,用于具有歇斯底里的非线性系统,确保在设定的时间内快速和准确的跟踪. 该方法克服了复杂性,并提高了相互连接系统的控制精度.

关键词:
适应性神经网络去中心化控制器输入的歇斯底里存在.大型互联系统大规模互联系统修改了命令过器的修改.预定义的时间稳定性.

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科学领域:

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

背景情况:

  • 大规模互连的非线性系统带来了重大的控制挑战.
  • 输入歇斯底里和系统不确定性降低了控制性能.
  • 现有的后退方法可能会遭受复杂性爆炸和喋喋不休.

研究的目的:

  • 开发一个预定义时间的自适应神经网络去中心化控制器.
  • 解决大规模互连的非线性系统与输入歇斯底里.
  • 为了保证在特定的结算时间内追踪错误的趋同.

主要方法:

  • 使用后退技术与修改的命令过器相结合.
  • 采用在线神经网络近似器用于系统不确定性.
  • 引入了一种新的预定义时间错误补偿机制.

主要成果:

  • 追踪错误在预定义的结算时间内汇聚到一个小的边界集.
  • 控制参数调整了收时间.
  • 有效地缓解了复杂性爆炸和聊天现象.
  • 神经网络补偿了系统的不确定性.

结论:

  • 拟议的控制器对于具有歇斯底里的非线性系统是可行的和有效的.
  • 实现了精确的控制,保证了收时间.
  • 为复杂的相互连接系统提供强大的解决方案.