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相关概念视频

Distributed Loads: Problem Solving01:21

Distributed Loads: Problem Solving

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Beams are structural elements commonly employed in engineering applications requiring different load-carrying capacities. The first step in analyzing a beam under a distributed load is to simplify the problem by dividing the load into smaller regions, which allows one to consider each region separately and calculate the magnitude of the equivalent resultant load acting on each portion of the beam. The magnitude of the equivalent resultant load for each region can be determined by calculating...
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Statically Indeterminate Problem Solving01:16

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Statically indeterminate problems are those where statics alone can not determine the internal forces or reactions. Consider a structure comprising two cylindrical rods made of steel and brass. These rods are joined at point B and restrained by rigid supports at points A and C. Now, the reactions at points A and C and the deflection at point B are to be determined. This rod structure is classified as statically indeterminate as the structure has more supports than are necessary for maintaining...
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Stability of Equilibrium Configuration: Problem Solving01:13

Stability of Equilibrium Configuration: Problem Solving

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The stability of equilibrium configurations is an important concept in physics, engineering, and other related fields. In simple terms, it refers to the tendency of an object or system to return to its equilibrium position after being disturbed. The stability of an equilibrium configuration can be analyzed by considering the potential energy function of the system and examining its behavior near the equilibrium point.
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One-Degree-of-Freedom System01:24

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In mechanical engineering, one-degree-of-freedom systems form the basis of a wide range of electrical and mechanical components. Using these models, engineers can predict the behavior of various parts in a larger system, which gives them insight into how different forces interact with each other.
A one-degree-of-freedom system is defined by an independent variable that determines its state and behavior. One example of a one-degree-of-freedom system is a simple harmonic oscillator, such as a...
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Three-Dimensional Force System:Problem Solving01:30

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A three-dimensional force system refers to a scenario in which three forces act simultaneously in three different directions. This type of problem is commonly encountered in physics and engineering, where it is necessary to calculate the resultant force on the system, which can then be used to predict or analyze the behavior of the object or structure under consideration.
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Multi-input and Multi-variable systems01:22

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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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优化分布式形成控制使用标识符-关键-行为体强化学习用于一类随机非线性多代理系统.

Guoxing Wen1, Ben Niu2

  • 1Shandong University of Aeronautics, Binzhou, 256600, Shandong, China.

ISA transactions
|October 29, 2024
PubMed
概括

本研究介绍了一种适应性强化学习 (RL) 方法,用于控制具有未知的动态的多代理系统 (MAS). 这种方法简化了对随机系统的最佳控制,从而实现了有效的分布式形成控制.

关键词:
多种药剂的形成.神经网络的神经网络最佳的控制控制是最好的控制.强化学习是一种强化学习.随机动态动态 随机动态动态

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科学领域:

  • 机器人技术 机器人技术 机器人技术
  • 控制理论 控制理论
  • 人工智能的人工智能

背景情况:

  • 多代理系统 (MAS) 经常面临未知的动态和随机环境的挑战.
  • 为了实现最佳控制,传统的强化学习 (RL) 可能是复杂的,并且由于依赖汉密尔顿-雅各比-贝尔曼 (HJB) 方程,难以应用于随机系统.

研究的目的:

  • 提出基于自适应强化学习 (RL) 的优化分布式训练控制策略.
  • 为了应对在随机非线性单集成器多代理系统 (MAS) 中未知动态的挑战.

主要方法:

  • 使用自适应识别神经网络 (NN) 来估计随机MAS的未知动态.
  • 实施一种强化学习 (RL) 方法,利用演员和批评神经网络,以优化形成控制.
  • 适应性RL定律是从一个正函数推导出来的,与基于HJB方程负梯度的方法相比,这简化了算法.

主要成果:

  • 提出的方法成功地识别了未知的系统动态在预期下.
  • 适应式RL框架使多代理系统的优化分布式形成控制成为可能.
  • 理论证明和计算机模拟验证了优化控制方案的有效性.

结论:

  • 开发的基于RL的自适应性形成控制对未知的随机非线性多代理系统有效.
  • 简化的RL算法允许在随机动态系统中更顺利地实现.
  • 该方法实现了分布式形成控制所需的控制目标.