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相关概念视频

Relative Motion Analysis using Rotating Axes-Problem Solving01:29

Relative Motion Analysis using Rotating Axes-Problem Solving

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Consider a crane whose telescopic boom rotates with an angular velocity of 0.04 rad/s and angular acceleration of 0.02 rad/s2. Along with the rotation, the boom also extends linearly with a uniform speed of 5 m/s. The extension of the boom is measured at point D, which is measured with respect to the fixed point C on the other end of the boom. For the given instant, the distance between points C and D is 60 meters.
Here, in order to determine the magnitude of velocity and acceleration for point...
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Time-Domain Interpretation of PD Control01:07

Time-Domain Interpretation of PD Control

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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...
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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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Feedback control systems01:26

Feedback control systems

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

Open and closed-loop control systems

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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...
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Relative Motion Analysis using Rotating Axes01:25

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Consider a component AB undergoing a linear motion. Along with a linear motion, point B also rotates around point A. To comprehend this complex movement, position vectors for both points A and B are established using a stationary reference frame.
However, to express the relative position of point B relative to point A, an additional frame of reference, denoted as x'y', is necessary. This additional frame not only translates but also rotates relative to the fixed frame, making it...
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相关实验视频

Updated: Mar 15, 2026

Investigating Motor Skill Learning Processes with a Robotic Manipulandum
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机器人操纵器的自适应预定义时间跟踪控制基于演员-关键强化学习.

Yong Qin1, Yuan Sun2, Jun Huang2

  • 1School of Artificial Intelligence and Smart Manufacturing, Hechi University, Hechi 546300, China.

Sensors (Basel, Switzerland)
|March 14, 2026
PubMed
概括

这项研究引入了一种新的预定义时间适应神经控制,用于使用Actor-Critic强化学习的不确定操纵者. 它确保了操纵器跟踪控制的快速,有保证的融合,优于PID方法.

关键词:
演员-关键的强化学习学习适应性神经网络控制控制后退步骤控制控制的控制方式预定义的时间控制控制器.

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科学领域:

  • 机器人技术 机器人技术 机器人技术
  • 控制系统 控制系统
  • 人工智能的人工智能

背景情况:

  • 操纵系统经常面临动态的不确定性,使精确的控制变得复杂.
  • 传统的控制方法可能会在复杂系统的快速融合和保证的结算时间方面扎.

研究的目的:

  • 为不确定的操纵系统开发一种新的预定义时间的自适应神经跟踪控制方法.
  • 将预定义时间稳定理论与强化学习相结合,以提高控制性能.
  • 为了实现快速融合,明确预设结算时间限制.

主要方法:

  • 使用神经网络的Actor-Critic强化学习框架.
  • 一个Actor网络接近未知的动态,并产生控制信号.
  • 关键网络通过评估成本-to-go函数来优化学习过程.
  • 将特定术语纳入控制法律和重量更新,以实现预定义时间的融合.
  • 运用利亚普诺夫稳定理论进行严格的分析.

主要成果:

  • 在预设的边界内证明了跟踪错误的预定义时间收.
  • 确保所有闭环信号的边界性.
  • 沉时间可通过单个设计参数进行调节,独立于初始条件.
  • 与传统的PID控制相比,模拟结果显示性能优越.

结论:

  • 提出的预定义时间的自适应神经控制方法对不确定的操纵系统是有效的.
  • 演员-批评强化学习框架成功实现了快速和稳定的跟踪控制.
  • 这种方法对机器人操纵器的现有控制策略提供了显著的改进.