Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Related Concept Videos

Torque On A Current Loop In A Magnetic Field

Torque On A Current Loop In A Magnetic Field

The most common application of magnetic force on current-carrying wires is in electric motors. These consist of loops of wire, which are placed between the magnets with a magnetic field. When current flows through the loops, the magnetic field applies torque, which causes the shaft to rotate, thus converting electrical energy to mechanical energy.
Consider a rectangular current-carrying loop containing N turns of wire, placed in a uniform magnetic field. The net force on a current-carrying loop...

Open and closed-loop control systems

Open and closed-loop control systems

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.
An open-loop control system operates without feedback from the output. It consists of two primary elements: the controller and the controlled process. The controller receives an input signal and...

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...

Electro-mechanical Systems

Electro-mechanical Systems

Electromechanical systems are intricate configurations that effectively combine electrical and mechanical elements to achieve a desired outcome. Central to many of these systems is the DC motor, a device that converts electrical energy into mechanical motion, enabling various applications ranging from simple fans to complex robotic mechanisms.
A key component of the DC motor is the armature, a rotating circuit positioned within a magnetic field. As an electric current passes through the...

Controller Configurations

Controller Configurations

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.
Control-system compensation involves various configurations, most commonly series or cascade compensation, in which the controller aligns...

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...

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by

Same author

Comparative Effects of Azadirachta indica-Derived Silver Nanoparticles and Silver Nitrate on the Productive and Reproductive Performance of Japanese Quails.

Veterinary medicine and science·2026

Same author

In Reply: Comment on "Nonsurgical Integrative Treatments for Degenerative Symptomatic Lumbar Spondylolisthesis".

Mayo Clinic proceedings·2026

Same author

Computational prediction of binding affinity and structural impact of three Pakistani SARS-CoV-2 spike RBD variants on human ACE2 interaction.

PloS one·2026

Same author

SceneHub4D: A Dataset and Evaluation Framework for 6-DoF 4D VR Scenes.

IEEE transactions on visualization and computer graphics·2026

Same author

Validation of the Eyerobo FC Portable Fundus Camera for Diabetic Retinopathy Screening Using Public Datasets and Deep Learning.

Ophthalmology and therapy·2026

Same author

Structure-preserving super-resolution of retinal fundus images via a dual-transformer residual network.

Frontiers in medicine·2026

Search research articles

Related Experiment Video

Updated: Jul 9, 2026

The Modular Design and Production of an Intelligent Robot Based on a Closed-Loop Control Strategy

The Modular Design and Production of an Intelligent Robot Based on a Closed-Loop Control Strategy

Published on: October 14, 2017

Model-free current control solution employing intelligent control for enhanced motor drive performance.

Muhammad Usama¹, Jaehong Kim²

¹Automation and System Division, ESIGELEC, Rouen, 76800, France.

Scientific Reports

|January 2, 2025

Summary

This study introduces a smart, model-free current control method that avoids complex models and reduces errors. The data-driven approach enhances performance, offering better results than traditional techniques.

Keywords:

Classification Feed-forward neural network Gating pulse Model-free current control Optimization SPMSM

More Related Videos

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

Design and Application of a Fault Detection Method Based on Adaptive Filters and Rotational Speed Estimation for an Electro-Hydrostatic Actuator

Design and Application of a Fault Detection Method Based on Adaptive Filters and Rotational Speed Estimation for an Electro-Hydrostatic Actuator

Published on: October 28, 2022

Related Experiment Videos

Last Updated: Jul 9, 2026

The Modular Design and Production of an Intelligent Robot Based on a Closed-Loop Control Strategy

The Modular Design and Production of an Intelligent Robot Based on a Closed-Loop Control Strategy

Published on: October 14, 2017

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

Design and Application of a Fault Detection Method Based on Adaptive Filters and Rotational Speed Estimation for an Electro-Hydrostatic Actuator

Design and Application of a Fault Detection Method Based on Adaptive Filters and Rotational Speed Estimation for an Electro-Hydrostatic Actuator

Published on: October 28, 2022

Area of Science:

Electrical Engineering
Control Systems
Artificial Intelligence

Background:

Conventional model predictive current control (MPCC) is computationally intensive and relies on precise plant models.
Plant parameter uncertainties and model discrepancies degrade the performance of traditional control systems.

Purpose of the Study:

To develop an intelligent, model-free current control strategy that reduces computational load and enhances robustness.
To improve dynamic performance by optimizing speed control loop parameters using adaptive particle swarm optimization (APSO).

Main Methods:

A data-driven approach is utilized, requiring fewer input features for efficient training.
Adaptive Particle Swarm Optimization (APSO) is employed to tune the gain parameters of the outer speed control loop.
A comparative analysis is conducted against a conventional control scheme to validate the proposed method.

Main Results:

The model-free data-driven approach accurately learned switching states from a model-based design with 94.8% accuracy.
The proposed method demonstrated superior steady-state performance compared to traditional approaches.
Lower harmonic distortion and increased robustness were observed with the data-driven control scheme.

Conclusions:

The proposed intelligent, model-free current control strategy effectively addresses plant model uncertainties and reduces computational burden.
The data-driven approach offers significant advantages over conventional methods in terms of performance, robustness, and efficiency.
The integration of APSO further enhances the dynamic performance of the control system.