基于完全代学习的外部干扰的非线性分布式参数系统的故障估计.
IEEE transactions on cybernetics
|July 30, 2025
概括
本研究提出了一种用于非线性系统中故障估计的新方法. 这种方法可以准确地识别时间和空间上的故障,即使是外部干扰.
科学领域:
- 控制系统工程 控制系统工程
- 非线性动力学是一种非线性动力学.
- 错误诊断 错误诊断 错误诊断 错误诊断 错误诊断 错误诊断
背景情况:
- 非线性分布式参数系统 (NDPS) 是复杂的,容易发生故障.
- 准确的故障估计对于系统可靠性和安全性至关重要.
- 外部干扰使故障检测和估计变得复杂.
研究的目的:
- 开发一种创新的方法,用于NDPSs的同时故障估计.
- 为了解决时域和时空断裂特征.
- 为了减轻外部干扰对故障估计准确性的影响.
主要方法:
- 一个代学习观察器被设计用于捕捉时间和空间变化.
- 完全代学习 (FIL) 技术用于错误估计规律的开发.
- 使用 λ-norm 方法来简化度分析和增益计算.
主要成果:
- 拟议的方法可以快速准确地估计故障信号.
- 这种方法有效地减轻了外部干扰的影响.
- 模拟结果证实了该方法在估计跨时间和时空领域的故障方面的效率.
结论:
- 开发的代学习观察器和FIL技术为NDPS中故障估计提供了有效的解决方案.
- λ-norm方法简化了理论分析和实际实施.
- 该方法在存在外部干扰的情况下显示出强大的性能.
相关概念视频
01:21Linear Approximation in Time Domain
125
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,...
For a simple pendulum with a mass evenly distributed along its length and the center of mass located at half the pendulum's length,...
125
01:26Linear Approximation in Frequency Domain
135
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....
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....
135
01:29Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving
101
Mechanistic models play a crucial role in algorithms for numerical problem-solving, particularly in nonlinear mixed effects modeling (NMEM). These models aim to minimize specific objective functions by evaluating various parameter estimates, leading to the development of systematic algorithms. In some cases, linearization techniques approximate the model using linear equations.
In individual population analyses, different algorithms are employed, such as Cauchy's method, which uses a...
In individual population analyses, different algorithms are employed, such as Cauchy's method, which uses a...
101
01:18Second Order systems II
173
In an underdamped second-order system, where the damping ratio ζ is between 0 and 1, a unit-step input results in a transfer function that, when transformed using the inverse Laplace method, reveals the output response. The output exhibits a damped sinusoidal oscillation, and the difference between the input and output is termed the error signal. This error signal also demonstrates damped oscillatory behavior. Eventually, as the system reaches a steady state, the error diminishes to zero.
173
01:24One-Compartment Open Model: Wagner-Nelson and Loo Riegelman Method for ka Estimation
717
This lesson introduces two critical methods in pharmacokinetics, the Wagner-Nelson and Loo-Riegelman methods, used for estimating the absorption rate constant (ka) for drugs administered via non-intravenous routes. The Wagner-Nelson method relates ka to the plasma concentration derived from the slope of a semilog percent unabsorbed time plot. However, it is limited to drugs with one-compartment kinetics and can be impacted by factors like gastrointestinal motility or enzymatic degradation.
On...
On...
717
01:10Propagation of Uncertainty from Systematic Error
887
The atomic mass of an element varies due to the relative ratio of its isotopes. A sample's relative proportion of oxygen isotopes influences its average atomic mass. For instance, if we were to measure the atomic mass of oxygen from a sample, the mass would be a weighted average of the isotopic masses of oxygen in that sample. Since a single sample is not likely to perfectly reflect the true atomic mass of oxygen for all the molecules of oxygen on Earth, the mass we obtain from this...
887