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State Space Representation01:27

State Space Representation

711
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.
Consider an RLC circuit, a...
711
Linear Approximation in Time Domain01:21

Linear Approximation in Time Domain

406
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,...
406
Linear Approximation in Frequency Domain01:26

Linear Approximation in Frequency Domain

432
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....
432
BIBO stability of continuous and discrete -time systems01:24

BIBO stability of continuous and discrete -time systems

1.1K
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....
1.1K
State Function, Exact and Inexact Differentials01:27

State Function, Exact and Inexact Differentials

111
A state function is a thermodynamic property that depends solely on the current state of a system, irrespective of its history or how it arrived at that state. These functions are represented by capital letters, such as U, H, and S, which stand for internal energy, enthalpy, and entropy, respectively.For instance, the value of internal energy depends on the system's state variables and remains unaffected by the process path. This means that whether the system underwent a linear process or a...
111
Sampling Continuous Time Signal01:11

Sampling Continuous Time Signal

836
In signal processing, a continuous-time signal can be sampled using an impulse-train sampling technique, followed by the zero-order hold method. Impulse-train sampling involves the use of a periodic impulse train, which consists of a series of delta functions spaced at regular intervals determined by the sampling period. When a continuous-time signal is multiplied by this impulse train, it generates impulses with amplitudes corresponding to the signal's values at the sampling points.
In the...
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Related Experiment Video

Updated: Mar 30, 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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Nonlinear Kalman filter based on duality relations between continuous and discrete-state stochastic processes.

Jun Ohkubo1

  • 1Graduate School of Science and Engineering, Saitama University, 255 Shimo-Okubo, Sakura, Saitama, 338-8570, Japan.

Physical Review. E, Statistical, Nonlinear, and Soft Matter Physics
|November 14, 2015
PubMed
Summary

This study demonstrates a novel application of duality relations in stochastic processes using numerical solutions. It shows potential for faster computational algorithms in estimating hidden variables.

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Area of Science:

  • Stochastic processes
  • Computational mathematics
  • Probability theory

Background:

  • Duality relations in stochastic processes typically require analytical solutions.
  • Estimating hidden variables in stochastic differential equations is a complex problem.

Purpose of the Study:

  • To demonstrate an alternative application of duality relations using numerical solutions.
  • To investigate the usefulness of numerical solutions for dual processes.
  • To explore faster computational algorithms for estimation problems.

Main Methods:

  • Numerical solutions of dual stochastic processes.
  • Algebraic probability theory to derive a birth-death process.
  • Ensemble Kalman filter for estimation.

Main Results:

  • A birth-death process was derived from stochastic differential equations.
  • An estimation method using the ensemble Kalman filter was proposed.
  • The potential for faster computational algorithms based on duality was shown.

Conclusions:

  • Numerical solutions offer a viable alternative for applying duality relations.
  • The proposed method shows promise for efficient estimation of hidden variables.
  • Duality concepts can lead to advancements in computational algorithms for stochastic systems.