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Related Concept Videos

PID Controller

PID Controller

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...

Time and frequency -Domain Interpretation of PI Control

Time and frequency -Domain Interpretation of PI Control

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...

Time-Domain Interpretation of PD Control

Time-Domain Interpretation of PD Control

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...

PI Controller: Design

PI Controller: Design

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...

PD Controller: Design

PD Controller: Design

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,...

Feedback control systems

Feedback control systems

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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Related Experiment Video

Updated: Jan 11, 2026

Interactive and Visualized Online Experimentation System for Engineering Education and Research

Interactive and Visualized Online Experimentation System for Engineering Education and Research

Published on: November 24, 2021

Event-triggered fuzzy neural-network PID control for nonlinear gas-blending processes.

Wenbo Dong¹, Songyuan Wang², Zhaozhao Zhang³

¹Beijing WenyanShun Technology Co., Ltd., 100085, Beijing, China. wenbo202506@163.com.

Scientific Reports

|November 17, 2025

Summary

A new event-triggered fuzzy neural network PID control method improves gas blending accuracy. This intelligent control system reduces frequent updates, enhancing performance and efficiency.

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Area of Science:

Control Engineering
Artificial Intelligence
Chemical Engineering

Background:

Gas blending systems often suffer from low control accuracy and excessive controller updates.
Traditional control methods struggle with the nonlinear dynamics inherent in gas concentration processes.

Purpose of the Study:

To propose an event-triggered fuzzy neural network PID control (ET-FNN-PID) method for enhanced gas blending.
To improve control accuracy and reduce the frequency of controller updates in gas blending applications.

Main Methods:

Developed a data-driven Takagi-Sugeno (TS) fuzzy neural network model using key operational variables.
Implemented an event-triggered mechanism with a fixed threshold to minimize controller actions.
Utilized gradient descent for online parameter tuning of the fuzzy neural network PID controller.

Main Results:

The ET-FNN-PID controller accurately captured nonlinear system behavior using real gas data.
Achieved precise gas concentration control with a significant reduction in update frequency compared to traditional methods.
Demonstrated superior performance over time-triggered and standard FNN-PID controllers.

Conclusions:

The proposed ET-FNN-PID method offers superior gas blending control performance.
This approach enhances energy efficiency and contributes to emission reduction through optimized control.
The event-triggered mechanism effectively reduces mechanical wear and computational load.