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相关概念视频

Open and closed-loop control systems01:17

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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.
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Gradually varying flow (GVF) in open channels describes situations where water depth changes slowly along the channel due to factors like non-uniform bed slope, channel shape variations, or obstructions. This flow type occurs when the depth adjusts gradually to balance gravitational forces, shear forces, and energy requirements, resulting in a low rate of depth change.Characteristics of Gradually Varying FlowGVF is commonly observed in natural streams, rivers, and canals, where flow depth...
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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.
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增量式多个子储水池回声状态网络控制不确定的通风过程.

Cuili Yang1, Qingrun Zhang1, Jiahang Zhang1

  • 1School of Information Science and Technology, Beijing Institute of Artificial Intelligence, Beijing University of Technology, Beijing, 100124, PR China.

Neural networks : the official journal of the International Neural Network Society
|December 17, 2025
PubMed
概括

一个新的增量多子储水池回声状态网络 (IMSESN) 控制器增强了废水处理中溶解氧的控制. 这种方法提高了不确定的通风过程的跟踪精度和计算效率.

关键词:
干扰观察者是干扰的观察者.一声状态网络网络的回声状态.追踪控制系统的追踪控制系统废水处理过程中的废水处理过程.

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科学领域:

  • 环境工程 环境工程
  • 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:

  • 由于非线性动态和未知的干扰,在废水处理过程 (WWTP) 中控制溶解氧 (DO) 具有挑战性.
  • 现有的方法在与空气化过程固有的不确定性作斗争.

研究的目的:

  • 提出一个先进的控制器,用于在WWTP中强大的溶解氧控制.
  • 为了提高适应性和减少通风控制中的计算复杂性.

主要方法:

  • 使用回声状态网络 (ESN) 作为系统状态近似器和无法测量的干扰的干扰观察器.
  • 整合一个以错误驱动的子储库增量机制,以提高不确定性近似度.
  • 应用最小学习参数 (MLP) 算法以优化权重更新和减少计算负载.
  • 采用利亚普诺夫稳定理论来证明闭环系统的稳定性.

主要成果:

  • 拟议的增量多子储水池回声状态网络 (IMSESN) 控制器与现有方法相比,显示出更高的跟踪精度.
  • 控制器表现出显著的计算效率,通过对基准模拟模型的模拟进行验证. 1 (BSM1) 的第一个部分.
  • 该系统在各种天气条件下表现出强性,在现实场景中显示出可靠的性能.

结论:

  • IMSESN控制器为WWTP中精确控制溶解氧气提供了有效的解决方案.
  • 开发的方法平衡了高控制精度和降低计算需求.
  • 这种方法为优化废水处理操作提供了有希望的进步.