Jove
Visualize
联系我们
JoVE
x logofacebook logolinkedin logoyoutube logo
关于 JoVE
概览领导团队博客JoVE 帮助中心
作者
出版流程编辑委员会范围与政策同行评审常见问题投稿
图书馆员
用户评价订阅访问资源图书馆顾问委员会常见问题
研究
JoVE JournalMethods CollectionsJoVE Encyclopedia of Experiments存档
教育
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab Manual教师资源中心教师网站
使用条款与条件
隐私政策
政策

相关概念视频

PID Controller01:19

PID Controller

115
Proportional-Integral-Derivative (PID) controllers are widely used in various control systems to enhance stability and performance. In a thermostat, it adjusts heating or cooling based on the temperature difference between the actual and desired levels. They are often used in automotive speed systems, effectively managing sudden speed changes while maintaining a constant speed under varying conditions. On the other hand, PI controllers, commonly employed in voltage regulation, enhance stability...
115
PD Controller: Design01:26

PD Controller: Design

222
In automotive engineering, car suspension systems often employ Proportional Derivative (PD) controllers to enhance performance. PD controllers are utilized to adjust the damping force in response to road conditions. A controller, acting as an amplifier with a constant gain, demonstrates proportional control, with output directly mirroring input.
Designing a continuous-data controller requires selecting and linking components like adders and integrators, which are fundamental in Proportional,...
222
PI Controller: Design01:24

PI Controller: Design

250
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...
250
Controller Configurations01:22

Controller Configurations

94
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...
94

您也可能阅读

相关文章

通过共同作者、期刊和引用图与本文相关的文章。

排序
Same author

Statistical Feature Engineering for Robot Failure Detection: A Comparative Study of Machine Learning and Deep Learning Classifiers.

Sensors (Basel, Switzerland)·2026
Same author

Trajectory Analysis of 6-DOF Industrial Robot Manipulators by Using Artificial Neural Networks.

Sensors (Basel, Switzerland)·2024
Same author

Deep Learning Based Apples Counting for Yield Forecast Using Proposed Flying Robotic System.

Sensors (Basel, Switzerland)·2023
查看所有相关文章

相关实验视频

Updated: Jun 26, 2025

Automated Deployment of an Internet Protocol Telephony Service on Unmanned Aerial Vehicles Using Network Functions Virtualization
07:49

Automated Deployment of an Internet Protocol Telephony Service on Unmanned Aerial Vehicles Using Network Functions Virtualization

Published on: November 26, 2019

8.0K

使用人工神经网络调整无人驾驶飞行器的比例-整体-衍生控制参数,以实现点对点轨迹的方法.

Burak Ulu1, Sertaç Savaş1, Ömer Faruk Ergin2

  • 1Department of Mechatronics Engineering, Erciyes University, 38039 Kayseri, Turkey.

Sensors (Basel, Switzerland)
|May 11, 2024
PubMed
概括

这项研究引入了一种新的神经网络方法,用于优化微型飞行器 (MAV) 中的比例-积分-导数 (PID) 控制器参数. 这种方法提高了轨迹控制的准确性,特别是在挑战性,受限制的环境中工作的四旋翼,如果园.

关键词:
农业技术 农业技术 农业技术自主导航自主导航自主导航神经网络的神经网络的神经网络控制轨道的轨道控制.无人驾驶飞行器 无人驾驶飞行器 无人驾驶飞行器

更多相关视频

Gain-compensation Methodology for a Sinusoidal Scan of a Galvanometer Mirror in Proportional-Integral-Differential Control Using Pre-emphasis Techniques
09:01

Gain-compensation Methodology for a Sinusoidal Scan of a Galvanometer Mirror in Proportional-Integral-Differential Control Using Pre-emphasis Techniques

Published on: April 4, 2017

8.7K
Interactive and Visualized Online Experimentation System for Engineering Education and Research
08:35

Interactive and Visualized Online Experimentation System for Engineering Education and Research

Published on: November 24, 2021

2.4K

相关实验视频

Last Updated: Jun 26, 2025

Automated Deployment of an Internet Protocol Telephony Service on Unmanned Aerial Vehicles Using Network Functions Virtualization
07:49

Automated Deployment of an Internet Protocol Telephony Service on Unmanned Aerial Vehicles Using Network Functions Virtualization

Published on: November 26, 2019

8.0K
Gain-compensation Methodology for a Sinusoidal Scan of a Galvanometer Mirror in Proportional-Integral-Differential Control Using Pre-emphasis Techniques
09:01

Gain-compensation Methodology for a Sinusoidal Scan of a Galvanometer Mirror in Proportional-Integral-Differential Control Using Pre-emphasis Techniques

Published on: April 4, 2017

8.7K
Interactive and Visualized Online Experimentation System for Engineering Education and Research
08:35

Interactive and Visualized Online Experimentation System for Engineering Education and Research

Published on: November 24, 2021

2.4K

科学领域:

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

背景情况:

  • 对无人驾驶微型飞行器 (MAV) 来说,轨道控制至关重要,特别是在风等严重干扰的环境中.
  • 比例积分导数 (PID) 控制器是轨迹控制的标准,但最佳增益调整对于高精度至关重要.
  • 传统的PID参数调节手动或自动调节方法对于在狭窄的空间 (如狭窄的果园) 操作的MAV是不切实际的.

研究的目的:

  • 开发一种创新的解决方案,用于最优的PID参数调整,专门用于限制环境中的四旋翼MAV.
  • 为了提高MAV在艰难地形上航行的轨迹控制的效率和准确性.
  • 解决现有的PID调方法在实际应用中的局限性,如果园监控.

主要方法:

  • 提出了一种基于神经网络的新方法来调整最佳PID控制参数.
  • 进行了飞行模拟,以评估拟议的神经网络模型的性能.
  • 前后传播网络 (FFBPN) 特别被调查其在轨迹跟踪方面的有效性.

主要成果:

  • 拟议的神经网络方法在模拟中证明了成功的轨迹跟踪性能.
  • 前向向后传播网络 (FFBPN) 显示出卓越的性能,特别是在度跟踪方面.
  • 对于度跟踪,实现了7.52745 × 10-5的平方根平均误差 (RMSE),表明了高精度.

结论:

  • 开发的基于神经网络的PID调方法显著提高了MAV的轨迹控制效率.
  • 这种方法在具有挑战性和受限制的环境中非常有效,为果园中的MAV提供了实际解决方案.
  • 模拟结果验证了高性能和在困难的操作环境下实现自动飞行能力的潜力.