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相关概念视频

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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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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Controller Configurations01:22

Controller Configurations

149
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...
149
Multi-input and Multi-variable systems01:22

Multi-input and Multi-variable systems

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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...
147
Feedback control systems01:26

Feedback control systems

416
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...
416
Control Systems: Applications01:25

Control Systems: Applications

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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...
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相关实验视频

Updated: Sep 9, 2025

The Modular Design and Production of an Intelligent Robot Based on a Closed-Loop Control Strategy
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基于模型预测控制的模块化可重新配置操纵器的事件触发最佳控制

Fan Zhou1, Yifan Zhang1, Tianhao Ma2

  • 1School of Electrical and Electronic Engineering, Changchun University of Technology, 130012, Changchun, China.

ISA transactions
|September 4, 2025
PubMed
概括

本研究引入了使用模型预测控制 (MPC) 的模块化可重新配置操纵器 (MRM) 的事件触发最佳控制. 该方法提高了性能和稳定性,同时通过扭矩限制和自适应动态编程确保了安全性.

关键词:
适应式动态编程事件触发控制模型预测控制可重新配置的模块化操纵器

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Last Updated: Sep 9, 2025

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科学领域:

  • 机器人和控制系统
  • 在自动化中的人工智能
  • 先进的控制理论

背景情况:

  • 模块化可重新配置操纵器 (MRM) 由于其可适应性结构而存在复杂的控制挑战.
  • 现有的控制方法往往难以实现分散的协调和对模型不确定性的稳定性.
  • 通过输入约束来确保安全对于实际的MRM应用至关重要.

研究的目的:

  • 开发一个事件触发的MRM最佳控制策略.
  • 提高系统的性能,稳定性和安全性.
  • 解决分散控制和MRM模型不准确的问题.

主要方法:

  • 一个分散的模型预测控制 (MPC) 方法将MRM控制分解为由全球框架协调的模块特定任务.
  • 过度触角函数用于输入扭矩限制,以防止安全危险.
  • 适应动态编程 (ADP) 与MPC集成,以提高对建模错误的稳定性.
  • 关键神经网络 (NN) 用于解决最佳控制解决方案的汉密尔顿-雅各比-贝尔曼 (HJB) 方程.
  • 莱普诺夫稳定性理论应用于保证轨迹跟踪错误的统一终极边界性 (UUB).

主要成果:

  • 拟议的事件触发的MPC方法显著减少了MRM的轨迹跟踪错误.
  • 通过高效,分散的控制策略将资源消耗降到最低.
  • 限制扭矩能力得到增强,提高了运行安全性.
  • 通过ADP和NN的整合,系统的稳定性得到了提高.

结论:

  • 开发的事件触发最佳控制方法为MRM提供了强大而有效的解决方案.
  • 这种方法有效地平衡了复杂机器人系统的性能,安全性和适应性.
  • 这项工作推进了模块化和可重新配置的机器人平台的控制状态.