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相关概念视频

Load-frequency control01:28

Load-frequency control

144
Load-frequency control (LFC) is vital for maintaining power system stability, ensuring that frequency and power flows remain within acceptable limits during load changes. Turbine-governor control eliminates rotor accelerations and decelerations following load changes. However, a steady-state frequency error persists when the change in the turbine-governor reference setting is zero. In an interconnected power system, each area agrees to export or import a scheduled amount of power through...
144
Linear Approximation in Time Domain01:21

Linear Approximation in Time Domain

76
Nonlinear systems often require sophisticated approaches for accurate modeling and analysis, with state-space representation being particularly effective. This method is especially useful for systems where variables and parameters vary with time or operating conditions, such as in a simple pendulum or a translational mechanical system with nonlinear springs.
For a simple pendulum with a mass evenly distributed along its length and the center of mass located at half the pendulum's length,...
76
Linear Approximation in Frequency Domain01:26

Linear Approximation in Frequency Domain

88
Linear systems are characterized by two main properties: superposition and homogeneity. Superposition allows the response to multiple inputs to be the sum of the responses to each individual input. Homogeneity ensures that scaling an input by a scalar results in the response being scaled by the same scalar.
In contrast, nonlinear systems do not inherently possess these properties. However, for small deviations around an operating point, a nonlinear system can often be approximated as linear....
88
Plotting and Calibrating the Root Locus01:19

Plotting and Calibrating the Root Locus

107
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...
107
Time and frequency -Domain Interpretation of Phase-lag Control01:21

Time and frequency -Domain Interpretation of Phase-lag Control

87
Phase-lag controllers are widely used in control systems to improve stability and reduce steady-state errors. A dimmer switch controlling the brightness of a light bulb serves as a practical example of phase-lag control, gradually adjusting the bulb's brightness. Mathematically, phase-lag control or low-pass filtering is represented when the factor 'a' is less than 1.
Phase-lag controllers do not place a pole at zero, but instead influence the steady-state error by amplifying any...
87
Time-Domain Interpretation of PD Control01:07

Time-Domain Interpretation of PD Control

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Proportional-Derivative (PD) control is a widely used control method in various engineering systems to enhance stability and performance. In a system with only proportional control, common issues include high maximum overshoot and oscillation, observed in both the error signal and its rate of change. This behavior can be divided into three distinct phases: initial overshoot, subsequent undershoot, and gradual stabilization.
Consider the example of control of motor torque. Initially, a positive...
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An Experimental Platform to Study the Closed-loop Performance of Brain-machine Interfaces
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最佳同步与 L2 - 获得性能:一种自适应动态编程方法.

Zitao Chen1, Kairui Chen2, Ruizhi Tang2

  • 1School of Mechanical and Electrical Engineering, Guangzhou University, Guangzhou, 510006, Guangdong, China; School of Mathematics, Southeast University, Nanjing, 211189, China; Department of Mathematical Sciences, Seoul National University, Seoul, 08826, Republic of Korea.

Neural networks : the official journal of the International Neural Network Society
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概括
此摘要是机器生成的。

这项研究为具有未知动态的非线性多代理系统提供了一个最佳的控制协议. 开发的方法确保了系统的稳定性,并实现了纳什平衡,以实现可靠的控制.

关键词:
- - 获得性能提升.适应性动态编程是适应性的.多代理系统多代理系统纳什平衡中的纳什平衡神经网络的神经网络的神经网络

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科学领域:

  • 控制理论 控制理论
  • 非线性系统是非线性系统.
  • 多代理系统 多代理系统

背景情况:

  • 设计非线性多代理系统的最佳同步控制是具有挑战性的,因为部分已知的动态和外部干扰.
  • 汉密尔顿 - 雅各比 - 艾萨克斯 (HJI) 方程为解决这些最佳控制问题提供了一个框架.

研究的目的:

  • 为非线性多代理系统开发一个最佳的同步控制协议.
  • 为了应对部分已知的动态和不确定的外部干扰所带来的挑战.
  • 为了实现强大的性能和稳定性.

主要方法:

  • 使用性能指数函数和系统动态,推导汉密尔顿 - 雅各比 - 艾萨克斯方程.
  • 应用分布式政策代自适应动态编程 (ADP) 用于HJI方程的数值解决方案.
  • 基于神经网络的控制协议实现的设计.

主要成果:

  • 证明了代变量对HJI方程解决方案的趋同.
  • 实现闭环系统的L2-gain性能,确保稳定性.
  • 导出控制协议被证明是纳什平衡的解决方案.

结论:

  • 提出的分布式ADP算法有效地解决了非线性多代理系统的最佳控制问题.
  • 该方法保证了系统的稳定性和在不确定的情况下的最佳性能.
  • 神经网络的实现为现实世界的应用提供了实用的方法.