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

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...

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,...

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...

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...

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...

Root-Locus Method

Root-Locus Method

A cruise control system in a car is designed to maintain a specified speed automatically by adjusting the gas pedal. The system continuously measures the vehicle's speed and makes fine adjustments to the pedal to achieve this goal. The root locus method is particularly useful for understanding how the cruise control system's behavior changes under varying conditions, such as when the car goes uphill, downhill, or faces strong wind resistance.
This system can be represented by a block...

You might also read

Related Articles

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

Sort by

Same author

A discrete enzyme action optimizer for traveling salesman problems with a smart tourism routing application: a case study in Bitlis, Türkiye.

Scientific reports·2026

Same author

RETRACTED: Srivastava et al. Match-Level Fusion of Finger-Knuckle Print and Iris for Human Identity Validation Using Neuro-Fuzzy Classifier. <i>Sensors</i> 2022, <i>22</i>, 3620.

Sensors (Basel, Switzerland)·2026

Same author

Robust Position-Only Null Steering in Linear Antenna Arrays via a Nature-Inspired Optimizer for Wireless Communication.

Biomimetics (Basel, Switzerland)·2026

Same author

Pressure Control of Centrifugal Fan Using Softsign-PI Controller Tuned by Hybrid Starfish Optimization Algorithm with Differential Evolution.

Biomimetics (Basel, Switzerland)·2026

Same author

A Biomimetic Gazelle Optimization Approach for Enhanced Temperature Regulation in Electric Furnaces.

Biomimetics (Basel, Switzerland)·2026

Same author

A novel hyperbolic tangent-augmented controller framework for temperature control in jacketed continuous stirred tank reactors.

Scientific reports·2026

Search research articles

Related Experiment Video

Updated: Jan 10, 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

Nonlinear 2-DOF PID controller optimized by artificial lemming algorithm for robust engine speed regulation in

Serdar Ekinci¹, Davut Izci^2,3, Mostafa Jabari⁴

¹Department of Computer Engineering, Bitlis Eren University, Bitlis, 13100, Turkey.

Scientific Reports

|November 26, 2025

Summary

A new nonlinear two-degree-of-freedom (2-DOF) PID controller optimized by the artificial lemming algorithm (ALA) significantly improves engine speed regulation in spark-ignition (SI) systems. This ALA-based controller offers superior accuracy and robustness against disturbances compared to other metaheuristic methods.

Keywords:

Artificial lemming algorithm Automotive control Disturbance rejection Engine speed regulation Metaheuristic optimization Nonlinear systems PID tuning Two-degree-of-freedom PID

More Related Videos

An Experimental Platform to Study the Closed-loop Performance of Brain-machine Interfaces

An Experimental Platform to Study the Closed-loop Performance of Brain-machine Interfaces

Published on: March 10, 2011

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

Related Experiment Videos

Last Updated: Jan 10, 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

An Experimental Platform to Study the Closed-loop Performance of Brain-machine Interfaces

An Experimental Platform to Study the Closed-loop Performance of Brain-machine Interfaces

Published on: March 10, 2011

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

Area of Science:

Control Systems Engineering
Automotive Engineering
Artificial Intelligence in Engineering

Background:

Precise engine speed regulation in spark-ignition (SI) internal combustion engines (ICEs) is challenging due to nonlinearities and disturbances.
Conventional proportional-integral-derivative (PID) controllers struggle with fast tracking and robust disturbance rejection in dynamic conditions.

Purpose of the Study:

To develop and optimize a nonlinear two-degree-of-freedom (2-DOF) PID controller for enhanced SI engine speed regulation.
To evaluate the performance of the artificial lemming algorithm (ALA) in optimizing controller gains for improved accuracy and robustness.

Main Methods:

A detailed mathematical model of an SI engine was used, including throttle, manifold pressure, combustion, and crankshaft dynamics.
The artificial lemming algorithm (ALA), a bio-inspired metaheuristic, was employed to optimize the 2-DOF PID controller gains.
A multi-term cost function minimizing overshoot, steady-state error, and stability coefficients was used for optimization.

Main Results:

The ALA demonstrated superior convergence stability compared to other tested metaheuristic algorithms.
The ALA-optimized controller achieved significantly reduced rise time (0.3114 s), settling time (2.4313 s), and negligible overshoot (0.0027%).
Exceptional steady-state error (2.62 × 10⁻¹¹%) and disturbance rejection capability (speed deviation < 0.5%) were achieved.

Conclusions:

The ALA-based nonlinear 2-DOF PID controller provides a robust, accurate, and energy-efficient solution for SI engine speed regulation.
The proposed controller outperforms existing metaheuristic-based approaches in accuracy and reliability.
The adaptive and scalable design is suitable for real-time embedded systems, hybrid powertrains, and other nonlinear control applications.