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Linear time-invariant Systems01:23

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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.
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The frequency-domain technique, commonly used in analyzing and designing feedback control systems, is effective for linear, time-invariant systems. However, it falls short when dealing with nonlinear, time-varying, and multiple-input multiple-output systems. The time-domain or state-space approach addresses these limitations by utilizing state variables to construct simultaneous, first-order differential equations, known as state equations, for an nth-order system.
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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...
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Related Experiment Video

Updated: Apr 25, 2026

Design and Application of a Fault Detection Method Based on Adaptive Filters and Rotational Speed Estimation for an Electro-Hydrostatic Actuator
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Dynamic neural network-based robust observers for uncertain nonlinear systems.

H T Dinh1, R Kamalapurkar2, S Bhasin3

  • 1Department of Mechanical Engineering, University of Transport and Communications, Viet Nam.

Neural Networks : the Official Journal of the International Neural Network Society
|August 17, 2014
PubMed
Summary
This summary is machine-generated.

A novel robust observer using dynamic neural networks (DNNs) accurately estimates states in uncertain nonlinear systems. This method, validated on a robot arm, outperforms existing state estimation techniques.

Keywords:
Lyapunov methodNeural networksOutput feedbackRobust adaptive control

Related Experiment Videos

Last Updated: Apr 25, 2026

Design and Application of a Fault Detection Method Based on Adaptive Filters and Rotational Speed Estimation for an Electro-Hydrostatic Actuator
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Design and Application of a Fault Detection Method Based on Adaptive Filters and Rotational Speed Estimation for an Electro-Hydrostatic Actuator

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Area of Science:

  • Control Systems Engineering
  • Robotics
  • Artificial Intelligence

Background:

  • Uncertain nonlinear systems pose significant challenges for state estimation.
  • Accurate state estimation is crucial for effective control and analysis of complex systems.

Purpose of the Study:

  • To develop a robust observer for uncertain nonlinear systems using dynamic neural networks (DNNs).
  • To ensure asymptotic convergence of estimated states to true system states.

Main Methods:

  • A dynamic neural network (DNN) estimates system dynamics online.
  • A dynamic filter estimates unmeasurable states.
  • A sliding mode feedback term compensates for uncertainties and disturbances.
  • Lyapunov-based analysis proves state convergence.

Main Results:

  • The proposed DNN-based observer demonstrates robust state estimation for high-order uncertain nonlinear systems.
  • Simulations and experiments on a two-link robot manipulator validate the observer's effectiveness.
  • The method shows superior performance compared to existing state estimation techniques.

Conclusions:

  • The developed observer provides a reliable solution for state estimation in challenging nonlinear systems.
  • This approach enhances the applicability of DNNs in robust control and system identification.
  • The method offers a promising alternative for real-world applications requiring accurate state feedback.