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Related Concept Videos

Impulse Response01:17

Impulse Response

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The impulse response is the system's reaction to an input impulse. In an RC circuit, the voltage source is the input, and the capacitor's voltage is the output. The system's state and output response before and after input excitation are distinctly defined.
Kirchhoff's law forms an input signal equation, with the capacitor's current and voltage providing the output. Substituting the current and dividing by RC yields a differential equation. The output for an impulse input is...
237
Transient and Steady-state Response01:24

Transient and Steady-state Response

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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...
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Convolution: Math, Graphics, and Discrete Signals01:24

Convolution: Math, Graphics, and Discrete Signals

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In any LTI (Linear Time-Invariant) system, the convolution of two signals is denoted using a convolution operator, assuming all initial conditions are zero. The convolution integral can be divided into two parts: the zero-input or natural response and the zero-state or forced response, with t0 indicating the initial time.
To simplify the convolution integral, it is assumed that both the input signal and impulse response are zero for negative time values. The graphical convolution process...
226
Deconvolution01:20

Deconvolution

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Deconvolution, also known as inverse filtering, is the process of extracting the impulse response from known input and output signals. This technique is vital in scenarios where the system's characteristics are unknown, and they must be inferred from the observable signals.
Deconvolution involves several mathematical techniques to derive the impulse response. One common approach is polynomial division. In this method, the input and output sequences are treated as coefficients of...
129
Feedback control systems01:26

Feedback control systems

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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...
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Time and frequency -Domain Interpretation of PI Control01:27

Time and frequency -Domain Interpretation of PI Control

104
Proportional-Integral (PI) controllers are essential in many control systems to improve stability and performance. They are commonly used in everyday devices like thermostats to enhance system damping and reduce steady-state error. When the zero in the controller's transfer function is optimally placed, the system benefits significantly in terms of stability and accuracy.
Acting as a low-pass filter, the PI controller slows the system's response and extends settling times. This requires...
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Discrete FIR filter-based Control.

John Cortés-Romero1, Brian Gómez-León2, Hebertt Sira-Ramírez3

  • 1Departamento de Ingeniería Eléctrica y Electrónica, Facultad de Ingeniería, Universidad Nacional de Colombia, Bogotá, Colombia.

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Summary

This study introduces a novel control system design integrating a Finite Impulse Response (FIR) filter and Internal Model Principle (IMP) for superior noise immunity and precise disturbance rejection. The method effectively balances responsiveness and noise suppression in control systems.

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FIR filterFlat filterNoise attenuation

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Area of Science:

  • Control Systems Engineering
  • Signal Processing
  • Mechatronics

Background:

  • Managing measurement noise is a critical challenge in control system design, often forcing a trade-off between responsiveness and noise suppression.
  • Traditional control methods frequently compromise either responsiveness or noise attenuation capabilities.

Purpose of the Study:

  • To propose a novel control system design that effectively decouples reference tracking dynamics from noise attenuation.
  • To enhance disturbance rejection and reference tracking accuracy while maintaining robustness against high-frequency noise.

Main Methods:

  • Integration of a Finite Impulse Response (FIR) filter within the discrete controller transfer function.
  • Application of the Internal Model Principle (IMP) for disturbance rejection.
  • Numerical analysis and experimental validation on a Programmable Logic Controller (PLC) and a DC-DC boost converter.

Main Results:

  • The proposed method demonstrates significant immunity to high-frequency noise compared to established control techniques.
  • Decoupling reference dynamics from noise attenuation ensures precise disturbance rejection and reference tracking.
  • Experimental validations confirm practical feasibility in real-world industrial scenarios, including high-noise environments.

Conclusions:

  • The novel control strategy offers a robust solution for managing measurement noise in control system design.
  • This approach provides a superior balance between system responsiveness and noise suppression, outperforming traditional methods.
  • The demonstrated applicability on a PLC and DC-DC boost converter highlights its potential for industrial adoption.