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Related Concept Videos

PID Controller01:19

PID Controller

269
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...
269
PD Controller: Design01:26

PD Controller: Design

374
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,...
374
PI Controller: Design01:24

PI Controller: Design

571
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...
571
Time and frequency -Domain Interpretation of PI Control01:27

Time and frequency -Domain Interpretation of PI Control

217
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...
217
Phase-lead and Phase-lag Controllers01:22

Phase-lead and Phase-lag Controllers

246
Understanding the working function of different types of controllers can be illustrated with practical analogies, such as adjusting a stereo's volume equalizer. Cranking up the bass involves a phase-lead controller, which functions as a high-pass filter, while increasing the treble uses a phase-lag controller, which acts as a low-pass filter. PD controllers, similar to high-pass filters, enhance the system's response to high-frequency components. PI controllers, akin to low-pass...
246
Time-Domain Interpretation of PD Control01:07

Time-Domain Interpretation of PD Control

192
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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A Design of FPGA-Based Neural Network PID Controller for Motion Control System.

Jun Wang1, Moudao Li1, Weibin Jiang1

  • 1College of Electrical Engineering and Automation, Fuzhou University, Fuzhou 350116, China.

Sensors (Basel, Switzerland)
|February 15, 2022
PubMed
Summary
This summary is machine-generated.

This study proposes a novel closed-loop motion control system using a field-programmable gate array (FPGA) and a neural network PID controller. This system offers superior real-time performance and adaptability for industrial applications compared to traditional microcontroller units (MCUs).

Keywords:
BPNNDC motorFPGAPIDPWMadaptive controlco-simulationspeed measurement

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Area of Science:

  • Control Systems Engineering
  • Artificial Intelligence in Automation
  • Embedded Systems Design

Background:

  • Traditional Proportional-Integral-Derivative (PID) controllers struggle with adaptively tuning parameters for diverse industrial control objects.
  • Existing microcontroller units (MCUs) often lack the real-time and high-reliability capabilities required for advanced control scenarios.
  • Neural network-based adaptive PID parameter tuning offers better performance but requires computationally powerful hardware.

Purpose of the Study:

  • To propose a closed-loop motion control system leveraging a Xilinx field-programmable gate array (FPGA) for enhanced control.
  • To implement a Backpropagation Neural Network (BPNN) PID controller within the FPGA for adaptive parameter tuning.
  • To address the real-time and reliability limitations of MCUs in industrial automation.

Main Methods:

  • A modular design approach was employed for the FPGA-based controller, including forward propagation, PID arithmetic, state machine, and weight update modules.
  • Peripheral modules for speed measurement (encoder signal acquisition) and motor speed control (Pulse Width Modulation - PWM generation) were developed.
  • Co-simulation using Modelsim and Simulink, followed by hardware testing on a development platform, was used for system verification.

Main Results:

  • The proposed system successfully achieved self-tuning of PID control parameters.
  • Demonstrated reliable performance, high real-time capability, and strong anti-interference characteristics.
  • Exhibited a convergence speed over three orders of magnitude faster than MCU-based systems.

Conclusions:

  • The FPGA-based BPNN PID controller provides a superior solution for adaptive motion control in industrial settings.
  • The system's enhanced real-time performance and adaptability overcome the limitations of traditional MCUs.
  • The proposed architecture validates the effectiveness of hardware acceleration for complex adaptive control algorithms.