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相关概念视频

Linear Approximation in Time Domain01:21

Linear Approximation in Time Domain

124
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,...
124
BIBO stability of continuous and discrete -time systems01:24

BIBO stability of continuous and discrete -time systems

511
System stability is a fundamental concept in signal processing, often assessed using convolution. For a system to be considered bounded-input bounded-output (BIBO) stable, any bounded input signal must produce a bounded output signal. A bounded input signal is one where the modulus does not exceed a certain constant at any point in time.
To determine the BIBO stability, the convolution integral is utilized when a bounded continuous-time input is applied to a Linear Time-Invariant (LTI) system....
511
Linear time-invariant Systems01:23

Linear time-invariant Systems

403
A system is linear if it displays the characteristics of homogeneity and additivity, together termed the superposition property. This principle is fundamental in all linear systems. Linear time-invariant (LTI) systems include systems with linear elements and constant parameters.
The input-output behavior of an LTI system can be fully defined by its response to an impulsive excitation at its input. Once this impulse response is known, the system's reaction to any other input can be...
403
Stability01:28

Stability

186
The time response of a linear time-invariant (LTI) system can be divided into transient and steady-state responses. The transient response represents the system's initial reaction to a change in input and diminishes to zero over time. In contrast, the steady-state response is the behavior that persists after the transient effects have faded.
The stability of an LTI system is determined by the roots of its characteristic equation, known as poles. A system is stable if it produces a bounded...
186
Pole and System Stability01:24

Pole and System Stability

419
The transfer function is a fundamental concept representing the ratio of two polynomials. The numerator and denominator encapsulate the system's dynamics. The zeros and poles of this transfer function are critical in determining the system's behavior and stability.
Simple poles are unique roots of the denominator polynomial. Each simple pole corresponds to a distinct solution to the system's characteristic equation, typically resulting in exponential decay terms in the system's...
419
Linear Approximation in Frequency Domain01:26

Linear Approximation in Frequency Domain

131
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....
131

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量子化下的线性系统的数据驱动超稳定

Jared Miller1,2, Jian Zheng2, Mario Sznaier2

  • 1J. Miller is with the Automatic Control Laboratory (IfA), Department of Information Technology and Electrical Engineering (D-ITET), ETH Zürich, Physikstrasse 3, 8092, Zürich, Switzerland.

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PubMed
概括
此摘要是机器生成的。

这项研究涉及用量子化数据稳定线性系统. 一种新的线性编程方法确保了系统稳定性,尽管传感器和输入量化,在示例系统上证明了有效性.

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科学领域:

  • 控制系统工程
  • 信息理论
  • 应用数学

背景情况:

  • 线性系统容易因数据量化而降低性能.
  • 在状态转换数据和控制输入中的量化对系统稳定提出了重大挑战.
  • 现有的方法通常在量子化下与非保守的稳定性作斗争.

研究的目的:

  • 用量子化状态转换数据和控制输入来稳定线性系统的强大方法.
  • 制定一种非保守的方法,考虑传感器量化和输入限制.
  • 确保所有系统的超级稳定性与观察到的量子化数据一致.

主要方法:

  • 使用基于强度的输入对数定量化稳定性的描述,以限于部门的不确定性.
  • 制定一个非保守的无限维线性程序.
  • 通过一对指数扩展的线性程序来解决问题.

主要成果:

  • 提出的方法成功地强制执行量子化线性系统的超稳定.
  • 无限维线性程序提供了一个非保守的解决方案.
  • 在各种量子化系统中证明了有效性.

结论:

  • 开发的线性编程技术为稳定量子化系统提供了强大的工具.
  • 在数据不确定性的情况下,这种方法提高了控制系统的可靠性.
  • 该方法推进了数字组件系统的可靠控制领域.