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相关概念视频

Feedback control systems01:26

Feedback control systems

267
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...
267
Multi-input and Multi-variable systems01:22

Multi-input and Multi-variable systems

93
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...
93
Open and closed-loop control systems01:17

Open and closed-loop control systems

600
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...
600
Control Systems01:10

Control Systems

998
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...
998
Control Systems: Applications01:25

Control Systems: Applications

547
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...
547
Time-Domain Interpretation of PD Control01:07

Time-Domain Interpretation of PD Control

78
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...
78

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通过使用自动编码器的动态隐藏变量来控制非线性过程的大数据驱动的控制.

Jun Wen Tang, Yitao Yan, Jie Bao

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    此摘要是机器生成的。

    本研究介绍了一种非线性系统的新型数据驱动控制方法,使用动态潜变量自编码器来简化复杂变量以进行有效控制. 这种方法确保了系统的稳定性和稳健性,而不需要理解因果关系.

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    科学领域:

    • 控制工程 控制工程 控制工程
    • 机器学习 机器学习
    • 系统理论 系统理论

    背景情况:

    • 非线性系统带来了重要的控制挑战.
    • 数据驱动的方法提供了传统基于模型的控制的替代方案.
    • 行为系统理论为分析没有明确因果关系的系统行为提供了一个框架.

    研究的目的:

    • 为非线性系统开发数据驱动的控制框架.
    • 将非线性物理变量投射到线性潜伏空间中.
    • 使用隐性变量来控制物理过程,而不假定因果关系.

    主要方法:

    • 使用动态隐性变量自编码器 (DLVAE) 进行维度缩小.
    • 实施基于潜在变量的数据预测控制策略.
    • 通过基于轨迹的消散性和利普希茨极限来确保稳定性和稳健性.

    主要成果:

    • 成功地将非线性物理变量投射到线性潜伏空间.
    • 通过隐性变量证明了对物理过程的有效控制.
    • 根据拟议的控制框架,建立了系统的稳定性和稳定性.

    结论:

    • 拟议的数据驱动方法为非线性系统控制提供了一种可行的方法.
    • 行为系统理论和DLVAE集成使无因果控制成为可能.
    • 该框架确保了稳定性和稳健性,适用于复杂的动态系统.