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Linear Approximation in Time Domain01:21

Linear Approximation in Time Domain

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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,...
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Time-Domain Interpretation of PD Control01:07

Time-Domain Interpretation of PD Control

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Proportional-Derivative (PD) control is a widely used control method in various engineering systems to enhance stability and performance. In a system with only proportional control, common issues include high maximum overshoot and oscillation, observed in both the error signal and its rate of change. This behavior can be divided into three distinct phases: initial overshoot, subsequent undershoot, and gradual stabilization.
Consider the example of control of motor torque. Initially, a positive...
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Linear time-invariant Systems01:23

Linear time-invariant Systems

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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.
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...
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Classification of Systems-II01:31

Classification of Systems-II

196
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,
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First Order Systems01:21

First Order Systems

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First-order systems, such as RC circuits, are foundational in understanding dynamic systems due to their straightforward input-output relationship. Analyzing their responses to different input functions under zero initial conditions reveals significant insights into system behavior.
When a first-order system is subjected to a unit-step input, its response is characterized by its transfer function. By applying the Laplace transform of the unit-step input to the transfer function, expanding the...
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Second Order systems II01:18

Second Order systems II

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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.
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Data-driven approach for time-delay estimation of industrial processes.

Xin-Yue Ma1, Chun-Qing Huang1

  • 1Department of Automation, Xiamen University, 361005, China.

ISA Transactions
|February 22, 2023
PubMed
Summary
This summary is machine-generated.

This study introduces a new data-driven method for estimating time delays in industrial processes using only output data. The approach accurately estimates delays without needing system identification or prior knowledge, validated on diverse examples.

Keywords:
Closed-loop controlData-drivenOnline estimationTime delay

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Area of Science:

  • Control Engineering
  • Signal Processing
  • Industrial Automation

Background:

  • Accurate time-delay estimation is critical for effective process control, performance assessment, and controller design.
  • Industrial processes often operate under routine conditions with background disturbances, requiring methods that utilize available closed-loop output data.
  • Existing methods may require system identification or prior process knowledge, limiting their applicability.

Purpose of the Study:

  • To develop a novel, data-driven approach for estimating time delays in industrial processes.
  • To provide practical solutions for time-delay estimation using only closed-loop output data under routine operating conditions.
  • To validate the proposed method's effectiveness on various numerical and industrial examples.

Main Methods:

  • A data-driven approach is proposed, estimating the closed-loop impulse response online from output data.
  • For large time delays, direct estimation is performed without system identification or prior knowledge.
  • For small time delays, estimation utilizes a stationarilized filter, pre-filter, and loop filter.

Main Results:

  • The proposed method successfully estimates time delays using only routine closed-loop output data.
  • The approach is effective for both large and small time-delayed processes.
  • Validation on industrial examples like distillation columns and refinery heating furnaces demonstrates practical applicability.

Conclusions:

  • The developed data-driven method offers an accurate and practical solution for time-delay estimation in industrial settings.
  • The approach bypasses the need for system identification and prior process knowledge, enhancing its versatility.
  • The method's effectiveness is confirmed across diverse industrial applications, highlighting its robustness.