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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.
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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.
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Controller Configurations

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Controller configurations are crucial in a car's cruise control system because they manage speed over time to maintain a consistent pace regardless of road conditions, thereby meeting design goals. In traditional control systems, fixed-configuration design involves predetermined controller placement. System performance modifications are known as compensation.
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Load-frequency control

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Load-frequency control (LFC) is vital for maintaining power system stability, ensuring that frequency and power flows remain within acceptable limits during load changes. Turbine-governor control eliminates rotor accelerations and decelerations following load changes. However, a steady-state frequency error persists when the change in the turbine-governor reference setting is zero. In an interconnected power system, each area agrees to export or import a scheduled amount of power through...

Time and frequency -Domain Interpretation of Phase-lead Control

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Phase-lead controllers are commonly used in various control systems to enhance response speed and stability. Adjusting the brightness on a television screen offers a practical example of phase-lead control. When contrast is enhanced, a phase-lead controller is employed. Mathematically, phase-lead control is identified when the first parameter is smaller than the second.
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The Modular Design and Production of an Intelligent Robot Based on a Closed-Loop Control Strategy

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Design of a novel robust adaptive fractional-order model predictive controller for boost converter using grey wolf

Chao Peng¹, Seyyed Morteza Ghamari², Hasan Mollaee³

¹College of Electrical Engineering and New Energy, THREE GORGES University, Yichang, China.

Scientific Reports

|July 29, 2025

Summary

This study introduces a Fractional-order adaptive Model Predictive Control (FO-MPC) for boost converters, enhancing stability and performance. The novel approach uses adaptive system identification and optimization for improved control accuracy and robustness.

Keywords:

Arduino DUE Boost converter ERLS identification Fractional calculus Grey wolf optimization Model predictive control

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Area of Science:

Power Electronics
Control Systems Engineering
Applied Mathematics

Background:

Boost converters are essential in power electronics but exhibit complex control challenges due to nonlinear dynamics and non-minimum phase behavior.
Conventional model predictive control (MPC) methods often require precise mathematical models, which are difficult to achieve in practical applications.

Purpose of the Study:

To propose a robust and adaptive control framework for boost converters that overcomes the limitations of traditional MPC.
To enhance controller performance, stability, and adaptability under varying operating conditions and parameter uncertainties.

Main Methods:

Development of a Fractional-order adaptive Model Predictive Control (FO-MPC) framework.
Integration of Exponential Regressive Least Squares (ERLS) for adaptive system identification, eliminating the need for exact models.
Inclusion of a Fractional-order (FO) derivative term to improve damping, stability, and noise immunity.
Utilization of Grey Wolf Optimization (GWO) for tuning FO-MPC parameters to optimize performance.

Main Results:

The proposed FO-MPC demonstrated superior performance, stability, and adaptability compared to PID and Fractional-order PID (FO-PID) controllers.
Adaptive modeling via ERLS improved robustness against parameter variations.
Experimental validation using an Arduino DUE setup confirmed the effectiveness of the FO-MPC framework.

Conclusions:

The FO-MPC offers a practical and effective solution for high-performance boost converter applications.
The adaptive nature and fractional-order elements significantly enhance control capabilities over conventional methods.