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Related Concept Videos

PD Controller: Design01:26

PD Controller: Design

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In automotive engineering, car suspension systems often employ Proportional Derivative (PD) controllers to enhance performance. PD controllers are utilized to adjust the damping force in response to road conditions. A controller, acting as an amplifier with a constant gain, demonstrates proportional control, with output directly mirroring input.
Designing a continuous-data controller requires selecting and linking components like adders and integrators, which are fundamental in Proportional,...
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PI Controller: Design01:24

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Proportional Integral (PI) controllers are a fundamental component in modern control systems, widely used to enhance performance and mitigate steady-state errors. They are particularly effective in applications such as automatic brightness adjustment on smartphones, where they excel at mitigating steady-state errors for step-function inputs. Unlike PD controllers, which require time-varying errors to function optimally, PI controllers leverage their integral component to address residual...
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Time-Domain Interpretation of PD Control01:07

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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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Time and frequency -Domain Interpretation of PI Control01:27

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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.
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Related Experiment Video

Updated: Oct 3, 2025

Fabrication and Characterization of Thickness Mode Piezoelectric Devices for Atomization and Acoustofluidics
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The Driving Waveform Design Method of Power-Law Fluid Piezoelectric Printing Based on Iterative Learning Control.

Ju Peng1, Jin Huang1, Jianjun Wang1

  • 1Key Laboratory of Electronic Equipment Structure Design of Ministry of Education, Xidian University, Xi'an 710071, China.

Sensors (Basel, Switzerland)
|February 15, 2022
PubMed
Summary

This study presents a novel driving waveform design method for piezoelectric 3D inkjet printing. It controls shear-thinning fluid flow for stable droplet generation, enhancing printing precision.

Keywords:
driving waveform designiterative learning controlpiezoelectric three-dimensional inkjet printing

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Area of Science:

  • Materials Science
  • Fluid Dynamics
  • Mechanical Engineering

Background:

  • Piezoelectric 3D inkjet printing often utilizes shear-thinning power-law fluids.
  • The time-varying viscosity of these fluids significantly impacts droplet formation and printing quality.
  • Precise control over the volume flow rate at the nozzle outlet is crucial for stable droplet generation.

Purpose of the Study:

  • To develop a driving waveform design method for piezoelectric 3D inkjet printing that accounts for the shear-thinning properties of power-law fluids.
  • To establish a method for controlling the volume flow rate at the nozzle outlet for improved printing effects.
  • To enable single and stable droplet generation during the printing process.

Main Methods:

  • Modified a model describing the inkjet mechanism of power-law fluids to establish the relationship between driving waveform and volume flow rate.
  • Developed a driving waveform design method utilizing iterative learning control.
  • Designed the iterative learning law based on the gradient descent algorithm and demonstrated its convergence.

Main Results:

  • Successfully established the relationship between driving waveform and volume flow rate for shear-thinning fluids.
  • Demonstrated the convergence of the iterative learning control method.
  • Verified the practicality and feasibility of the driving waveform design method through drop generation experiments.

Conclusions:

  • The proposed driving waveform design method effectively controls the volume flow rate of shear-thinning fluids in piezoelectric 3D inkjet printing.
  • This method provides a foundation for achieving single and stable droplet generation, leading to finer printing effects.
  • Experimental validation confirms the method's practical applicability in advanced manufacturing processes.