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相关概念视频

Feedback control systems01:26

Feedback control systems

319
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...
319
Time-Domain Interpretation of PD Control01:07

Time-Domain Interpretation of PD Control

123
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...
123
Linear Approximation in Time Domain01:21

Linear Approximation in Time Domain

85
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,...
85
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
Open and closed-loop control systems01:17

Open and closed-loop control systems

768
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...
768
Controller Configurations01:22

Controller Configurations

102
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...
102

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相关实验视频

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WheelCon: A Wheel Control-Based Gaming Platform for Studying Human Sensorimotor Control
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基于安全增强学习的受约束离散时间非线性系统的最佳控制.

Lingzhi Zhang, Lei Xie, Yi Jiang

    IEEE transactions on neural networks and learning systems
    |October 31, 2023
    PubMed
    概括

    本研究介绍了一种安全的强化学习 (RL) 方法,使用屏障函数来解决受约束的非线性系统的最佳控制问题. 该方法确保满足状态和输入约束,同时保持趋同和最佳性.

    科学领域:

    • 控制工程 控制工程 控制工程
    • 人工智能的人工智能
    • 非线性系统动态 非线性系统动态

    背景情况:

    • 非线性系统的最佳控制受到状态和输入约束的挑战.
    • 使用二次级实用函数的传统强化学习 (RL) 方法与这些限制作斗争.

    研究的目的:

    • 为使用安全RL的受约束离散时间 (DT) 非线性系统开发一种新的最佳控制方法.
    • 解决处理复杂系统约束的现有方法的局限性.

    主要方法:

    • 引入与值函数集成的障碍函数 (BF),将受约束的问题转换为不受约束的问题.
    • 开发一个受约束的政策代 (PI) 算法,利用两个神经网络 (NN) 来进行政策和价值函数近似.

    主要成果:

    • 提出的方法有效地将受约束的优化转化为一个不受约束的问题,在源头上有保证的最小值.
    • 受约束的PI算法成功满足非线性系统的状态和输入约束.
    • 这种方法保留了传统PI算法的收性和最佳性属性.

    结论:

    • 具有屏障功能的新型安全RL方法为受约束的DT非线性系统的最佳控制提供了有效的解决方案.

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  • 神经网络的使用有助于推导受约束的最佳控制策略.
  • 模拟结果证明了拟议方法的实际有效性.