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Related Concept Videos

Linear time-invariant Systems01:23

Linear time-invariant Systems

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 calculated...
Propagation of Uncertainty from Random Error00:59

Propagation of Uncertainty from Random Error

An experiment often consists of more than a single step. In this case, measurements at each step give rise to uncertainty. Because the measurements occur in successive steps, the uncertainty in one step necessarily contributes to that in the subsequent step. As we perform statistical analysis on these types of experiments, we must learn to account for the propagation of uncertainty from one step to the next. The propagation of uncertainty depends on the type of arithmetic operation performed on...
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.
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...
Feedback control systems01:26

Feedback control systems

Feedback control systems are categorized in various ways based on their design, analysis, and signal types.
Linear feedback systems are theoretical models that simplify analysis and design. These systems operate under the principle that their output is directly proportional to their input within certain ranges. For instance, an amplifier in a control system behaves linearly as long as the input signal remains within a specific range. However, most physical systems exhibit inherent nonlinearity...
Control Systems01:10

Control Systems

Control systems are everywhere in contemporary society, influencing diverse applications from aerospace to automated manufacturing. These systems can be found naturally within biological processes, such as blood sugar regulation and heart rate adjustment in response to stress, as well as in man-made systems like elevators and automated vehicles. A control system is essentially a network of subsystems and processes that collaboratively convert specific inputs into desired outputs.
At the heart...

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Related Experiment Videos

Observer-based H(infinity) control for networked nonlinear systems with random packet losses.

Jian Guo Li1, Jing Qi Yuan, Jun Guo Lu

  • 1Department of Automation, Shanghai Jiao Tong University, Shanghai 200240, People's Republic of China.

ISA Transactions
|August 29, 2009
PubMed
Summary
This summary is machine-generated.

This study addresses networked nonlinear systems with random packet loss, developing an observer-based H(infinity) controller for stability and disturbance rejection. The method ensures reliable control despite communication uncertainties.

Related Experiment Videos

Area of Science:

  • Control Systems Engineering
  • Networked Systems Theory
  • Nonlinear Dynamics

Background:

  • Networked systems face challenges from random packet losses impacting control performance.
  • Observer-based H(infinity) control is crucial for stability and disturbance rejection in nonlinear systems.

Purpose of the Study:

  • To investigate observer-based H(infinity) control for nonlinear systems with global Lipschitz nonlinearities and random packet losses.
  • To develop sufficient conditions for controller existence ensuring stability and performance.
  • To present a linear matrix inequality (LMI) approach for controller design.

Main Methods:

  • Modeling random packet loss using a Bernoulli distributed white sequence.
  • Deriving sufficient conditions for exponential mean-square stability and H(infinity) performance.
  • Utilizing a linear matrix inequality (LMI) framework for controller synthesis.

Main Results:

  • Sufficient conditions for the existence of a stabilizing observer-based H(infinity) controller were established.
  • The closed-loop system achieves exponential mean-square stability.
  • Prescribed H(infinity) disturbance-rejection-attenuation performance is guaranteed.

Conclusions:

  • The proposed observer-based H(infinity) control method effectively handles random packet losses in networked nonlinear systems.
  • The LMI approach provides a systematic way to design controllers ensuring stability and performance.
  • Simulation results validate the effectiveness of the developed control strategy.