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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

407
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...
407
Root-Locus Method01:19

Root-Locus Method

160
A cruise control system in a car is designed to maintain a specified speed automatically by adjusting the gas pedal. The system continuously measures the vehicle's speed and makes fine adjustments to the pedal to achieve this goal. The root locus method is particularly useful for understanding how the cruise control system's behavior changes under varying conditions, such as when the car goes uphill, downhill, or faces strong wind resistance.
This system can be represented by a block...
160
Time and frequency -Domain Interpretation of Phase-lead Control01:24

Time and frequency -Domain Interpretation of Phase-lead Control

88
Phase-lead controllers are commonly used in various control systems to enhance response speed and stability. Adjusting the brightness on a television screen offers a practical example of phase-lead control. When contrast is enhanced, a phase-lead controller is employed. Mathematically, phase-lead control is identified when the first parameter is smaller than the second.
The design of phase-lead control involves the strategic placement of poles and zeros to balance steady-state error and system...
88
Plotting and Calibrating the Root Locus01:19

Plotting and Calibrating the Root Locus

123
Root loci often diverge as system poles shift from the real axis to the complex plane. Key points in this transition are the breakaway and break-in points, indicating where the root locus leaves and reenters the real axis. The branches of the root locus form an angle of 180/n degrees with the real axis, where n is the number of branches at a breakaway or break-in point.
The maximum gain occurs at the breakaway points between open-loop poles on the real axis, while the minimum gain is...
123
Phase-lead and Phase-lag Controllers01:22

Phase-lead and Phase-lag Controllers

176
Understanding the working function of different types of controllers can be illustrated with practical analogies, such as adjusting a stereo's volume equalizer. Cranking up the bass involves a phase-lead controller, which functions as a high-pass filter, while increasing the treble uses a phase-lag controller, which acts as a low-pass filter. PD controllers, similar to high-pass filters, enhance the system's response to high-frequency components. PI controllers, akin to low-pass...
176
PI Controller: Design01:24

PI Controller: Design

293
Proportional Integral (PI) controllers are a fundamental component in modern control systems, widely used to enhance performance and mitigate steady-state errors. They are particularly effective in applications such as automatic brightness adjustment on smartphones, where they excel at mitigating steady-state errors for step-function inputs. Unlike PD controllers, which require time-varying errors to function optimally, PI controllers leverage their integral component to address residual...
293

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Operation of the Collaborative Composite Manufacturing CCM System
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基于PPO路径优化的一致的回合策略,用于领导-追随者跟踪问题.

Xiao Wang1, Zhaohui Yang1, Xueqian Bai1

  • 1College of Information Science and Technology, Beijing University of Chemical Technology, Beijing 100029, China.

Sensors (Basel, Switzerland)
|November 14, 2023
PubMed
概括

本研究介绍了一种合作的多无人机 (UAV) 战略,用于目标追踪和避难障碍. 这种新的方法确保了高的任务成功率,即使没有最初的目标检测.

关键词:
无人机飞行器的形成控制控制一致性协议的一致性协议强化学习是一种强化学习.

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科学领域:

  • 机器人和控制系统 机器人和控制系统
  • 人工智能和机器学习
  • 航空航天工程 航空航天工程

背景情况:

  • 单个无人机在复杂的任务中具有局限性,影响任务的成功和生存能力.
  • 多个无人机系统的传统控制方法在不稳定的环境中难以适应,需要大量的环境数据.
  • 合作的多无人机组合提供了增强的能力,但面临着可扩展性和动态条件的挑战.

研究的目的:

  • 为多无人机系统制定强大且适应性的控制策略,以克服单个无人机和传统方法的局限性.
  • 通过合作形成控制,提高无人机的任务成功率和生存能力.
  • 在没有事先检测目标的情况下,在动态环境中实现有效的目标跟踪和避开障碍.

主要方法:

  • 为路径优化和目标跟踪提出了使用近接政策优化 (PPO) 进行一致的总结策略.
  • 训练了一名领导无人机使用PPO进行自主避障和目标追踪.
  • 开发了一个通信网络,让追随者无人机从领导者那里接收环境信息,从而实现基于一致性协议和阿波罗的圆圈的追踪控制法.

主要成果:

  • 拟议的战略成功地实现了目标UAV围捕和避开障碍的目标,为追求多个UAV集团.
  • 该系统即使没有初始目标检测,也证明了它的有效性.
  • 模拟场景显示,多无人机集群在目标的圆形化中,成功率始终在80%以上.

结论:

  • 基于PPO的一致的回合策略显著提高了多无人机系统的性能和适应性.
  • 合作控制和通信网络对于在复杂,动态的环境中增强无人机能力至关重要.
  • 开发的方法为在具有挑战性的场景中实现自主目标追逐和导航提供了有希望的解决方案.