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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.
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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.
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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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Open and closed-loop control systems01:17

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Control systems are foundational elements in automation and engineering. They are broadly categorized into open-loop and closed-loop systems. These classifications hinge on the presence or absence of feedback mechanisms, significantly influencing the system's performance, complexity, and application.
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Proportional-Integral-Derivative (PID) controllers are widely used in various control systems to enhance stability and performance. In a thermostat, it adjusts heating or cooling based on the temperature difference between the actual and desired levels. They are often used in automotive speed systems, effectively managing sudden speed changes while maintaining a constant speed under varying conditions. On the other hand, PI controllers, commonly employed in voltage regulation, enhance stability...
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Updated: Aug 10, 2025

Measurement of Vibration Detection Threshold and Tactile Spatial Acuity in Human Subjects
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Ultraprecise Controller for Piezoelectric Actuators Based on Deep Learning and Model Predictive Control.

Jokin Uralde1, Eneko Artetxe1, Oscar Barambones1

  • 1Department of Systems Engineering and Automatic Control, Faculty of Engineering of Vitoria-Gasteiz, University of the Basque Country (UPV/EHU), 01006 Vitoria-Gasteiz, Spain.

Sensors (Basel, Switzerland)
|February 11, 2023
PubMed
Summary
This summary is machine-generated.

This study introduces a model-based predictive control (MPC) strategy using artificial neural networks (ANN) to improve the accuracy of piezoelectric actuators (PEA). The MPC approach effectively mitigates hysteresis, enhancing precision in micrometric displacement applications.

Keywords:
control systemshysteresismodel predictive controller (MPC)neural networkspiezoelectric actuators

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Area of Science:

  • Control Systems Engineering
  • Materials Science
  • Robotics and Automation

Background:

  • Piezoelectric actuators (PEAs) are crucial for high-precision applications requiring micrometric displacements.
  • Non-linearity, particularly hysteresis, significantly degrades PEA performance and accuracy.
  • Developing precise mathematical models for PEAs is challenging.

Purpose of the Study:

  • To present a high-precision control scheme for PEAs using model-based predictive control (MPC).
  • To leverage artificial neural networks (ANN) for simplified PEA modeling within the MPC framework.
  • To experimentally validate the effectiveness of the proposed MPC strategy against traditional PID control.

Main Methods:

  • Implemented an MPC control scheme fed by an ANN-derived model of the PEA.
  • Integrated the control system onto the dSPACE control platform.
  • Conducted experimental tests on a commercial Thorlabs PEA.
  • Compared MPC performance against a proportional-integral-derivative (PID) controller.

Main Results:

  • The ANN-based MPC strategy demonstrated superior accuracy in high-precision PEA applications.
  • MPC effectively managed non-linearity and hysteresis, outperforming PID control.
  • Experimental results confirmed improved tracking of periodic and sudden reference signals.

Conclusions:

  • The proposed ANN-based MPC approach offers a robust and accurate solution for controlling PEAs.
  • This method simplifies PEA modeling while enhancing precision in demanding applications.
  • The findings suggest significant potential for MPC in advanced actuator control systems.