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State Space Representation01:27

State Space Representation

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

BIBO stability of continuous and discrete -time systems

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.
Classification of Systems-II01:31

Classification of Systems-II

Continuous-time systems have continuous input and output signals, with time measured continuously. These systems are generally defined by differential or algebraic equations. For instance, in an RC circuit, the relationship between input and output voltage is expressed through a differential equation derived from Ohm's law and the capacitor relation,
Transient and Steady-state Response01:24

Transient and Steady-state Response

In control systems, test signals are essential for evaluating performance under various conditions. The ramp function is effective for systems undergoing gradual changes, while the step function is suitable for assessing systems facing sudden disturbances. For systems subjected to shock inputs, the impulse function is the most appropriate test signal.
These test signals are integral in designing control systems to exhibit two key performance aspects: transient response and steady-state response.
Propagation of Uncertainty from Systematic Error01:10

Propagation of Uncertainty from Systematic Error

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 particular...
Classification of Systems-I01:26

Classification of Systems-I

Linearity is a system property characterized by a direct input-output relationship, combining homogeneity and additivity.
Homogeneity dictates that if an input x(t) is multiplied by a constant c, the output y(t) is multiplied by the same constant. Mathematically, this is expressed as:

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Related Experiment Video

Updated: Jun 18, 2026

A Precise and Autonomous System for the Detection of Insect Emergence Patterns
06:22

A Precise and Autonomous System for the Detection of Insect Emergence Patterns

Published on: January 9, 2019

State Estimation and Detectability of Probabilistic Discrete Event Systems.

Shaolong Shu1, Feng Lin, Hao Ying

  • 1School of Electronics and Information Engineering, Tongji University, Shanghai, China.

Automatica : the Journal of IFAC, the International Federation of Automatic Control
|December 4, 2009
PubMed
Summary

This study introduces methods for state estimation in probabilistic discrete event systems (PDES). It develops conditions for probabilistic detectability by analyzing event sequence convergence in PDES.

Related Experiment Videos

Last Updated: Jun 18, 2026

A Precise and Autonomous System for the Detection of Insect Emergence Patterns
06:22

A Precise and Autonomous System for the Detection of Insect Emergence Patterns

Published on: January 9, 2019

Area of Science:

  • Control Theory
  • Computer Science
  • Systems Engineering

Background:

  • State estimation for non-probabilistic discrete event systems (DES) was previously studied, defining four types of detectabilities.
  • Probabilistic discrete event systems (PDES) introduce complexities due to specified transition probabilities.
  • Existing methods for state estimation in PDES are limited, especially concerning probabilistic aspects.

Purpose of the Study:

  • To extend state estimation techniques to probabilistic discrete event systems (PDES).
  • To define and derive conditions for probabilistic detectability in PDES.
  • To address challenges in state estimation for PDES with complete event observation and no state observation.

Main Methods:

  • Converting PDES to a nondeterministic discrete event system to establish sufficient conditions for probabilistic detectability.
  • Investigating the concept of 'convergence' in PDES event sequences.
  • Deriving necessary and sufficient conditions for probabilistic detectability based on event sequence convergence.

Main Results:

  • Sufficient conditions for probabilistic detectability were found by transforming PDES.
  • The concept of event sequence convergence was formally defined and analyzed for PDES.
  • Necessary and sufficient conditions for probabilistic detectability were established using convergence criteria.

Conclusions:

  • The study successfully extends state estimation for PDES by incorporating probabilities.
  • Event sequence convergence is a key factor in determining probabilistic detectability.
  • The developed framework provides a theoretical basis for state estimation in complex PDES.