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

Second Order systems I

Second Order systems I

A servo system exemplifies a second-order system, featuring a proportional controller and load elements that ensure the output position aligns with the input position. The relationship between these components is described by a second-order differential equation. Applying the Laplace transform under zero initial conditions yields the transfer function, showing how inputs are converted to outputs in the system.
By reinterpreting the system, one can derive the closed-loop transfer function, which...

Second Order systems II

Second Order systems II

In an underdamped second-order system, where the damping ratio ζ is between 0 and 1, a unit-step input results in a transfer function that, when transformed using the inverse Laplace method, reveals the output response. The output exhibits a damped sinusoidal oscillation, and the difference between the input and output is termed the error signal. This error signal also demonstrates damped oscillatory behavior. Eventually, as the system reaches a steady state, the error diminishes to zero.

Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving

Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving

Mechanistic models play a crucial role in algorithms for numerical problem-solving, particularly in nonlinear mixed effects modeling (NMEM). These models aim to minimize specific objective functions by evaluating various parameter estimates, leading to the development of systematic algorithms. In some cases, linearization techniques approximate the model using linear equations.
In individual population analyses, different algorithms are employed, such as Cauchy's method, which uses a...

Second-order Op Amp Circuits

Second-order Op Amp Circuits

Implementing second-order low-pass filters in audio systems is crucial in refining audio signals by eliminating undesirable high-frequency noise. These filters typically involve second-order op-amp circuits configured as voltage followers, encompassing two nodes with distinct storage elements.
The analysis of such circuits follows a systematic approach, similar to the second-order RLC circuits. In practical scenarios, bulky inductors are rarely employed due to their size and weight. This means...

Control System Problem

Control System Problem

In an open-loop system, such as a basic thermostat, the poles of the transfer function influence the system's response but do not determine its stability. However, when feedback is introduced to form a closed-loop system, such as an advanced thermostat that adjusts heating based on room temperature, stability is governed by the new poles of the closed-loop transfer function.
When forming a closed-loop system, issues can arise if the poles cross into the unstable region, leading to potential...

One-Compartment Open Model: Wagner-Nelson and Loo Riegelman Method for ka Estimation

One-Compartment Open Model: Wagner-Nelson and Loo Riegelman Method for k_a Estimation

This lesson introduces two critical methods in pharmacokinetics, the Wagner-Nelson and Loo-Riegelman methods, used for estimating the absorption rate constant (ka) for drugs administered via non-intravenous routes. The Wagner-Nelson method relates ka to the plasma concentration derived from the slope of a semilog percent unabsorbed time plot. However, it is limited to drugs with one-compartment kinetics and can be impacted by factors like gastrointestinal motility or enzymatic degradation.
On...

You might also read

Related Articles

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

Sort by

Same author

Smart Materials Employed in the Construction Industry: A Systematic Review of Types, Properties, Applications, and Sustainability Performance.

Materials (Basel, Switzerland)·2026

Same author

Cannabidiol Encapsulation in Polymeric Hydrogels and Its Controlled Release: A Review.

Gels (Basel, Switzerland)·2025

Same author

Sensory-Cognitive Profiles in Children with ADHD: Exploring Perceptual-Motor, Auditory, and Oculomotor Function.

Bioengineering (Basel, Switzerland)·2025

Same author

The Integration of Artificial Intelligence with Micro-Nano-Systems: Perspectives, Challenges and Future Prospects.

Micromachines·2025

Same author

Model Parametrization-Based Genetic Algorithms Using Velocity Signal and Steady State of the Dynamic Response of a Motor.

Biomimetics (Basel, Switzerland)·2025

Same author

Electromyography Signals in Embedded Systems: A Review of Processing and Classification Techniques.

Biomimetics (Basel, Switzerland)·2025

Search research articles

Related Experiment Video

Updated: Oct 27, 2025

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

Estimation of Transfer Function Coefficients for Second-Order Systems via Metaheuristic Algorithms.

Omar Rodríguez-Abreo^1,2, Juvenal Rodríguez-Reséndiz^2,3, Francisco Antonio Castillo Velásquez^1,4

¹Industrial Technologies Division, Universidad Politecnica de Queretaro, El Marques 76240, Mexico.

Sensors (Basel, Switzerland)

|July 20, 2021

Summary

This study introduces a novel method using metaheuristic algorithms and the Final Value Theorem for accurate parametric estimation of second-order transfer functions in stable systems. The approach enhances convergence speed and reduces errors, offering a versatile alternative to traditional methods.

Keywords:

Gray Wolf Optimizer Jaya algorithm metaheuristic parameter estimation transfer function

More Related Videos

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

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

Related Experiment Videos

Last Updated: Oct 27, 2025

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

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

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

Area of Science:

Control Systems Engineering
Computational Intelligence
System Identification

Background:

Accurate system modeling is crucial for control system design and analysis.
Parametric estimation of transfer functions, particularly for second-order systems, is a fundamental challenge.
Existing methods may require prior knowledge of system damping or lack generalizability.

Purpose of the Study:

To develop a general and accurate method for parametric estimation of second-order transfer functions using metaheuristic algorithms.
To leverage the Final Value Theorem to enhance the efficiency and accuracy of the estimation process.
To demonstrate the method's applicability to various stable systems, regardless of their order or damping characteristics.

Main Methods:

Utilizing metaheuristic algorithms (Gray Wolf, Jaya) for parametric estimation.
Employing the step response of systems with known amplitude for estimation.
Incorporating the Final Value Theorem as a constraint to accelerate convergence and reduce error.
Validating the method on electrical, mechanical, and electromechanical systems using both simulated and real-world signals.

Main Results:

The proposed method successfully estimates second-order transfer functions for systems of any order.
The Final Value Theorem significantly accelerates convergence, reducing errors up to 10 times in early iterations.
The method eliminates the need to pre-determine system damping, unlike analytical approaches.
The Gray Wolf Algorithm demonstrated superior performance, achieving up to 50% lower error compared to the Jaya algorithm.

Conclusions:

The developed metaheuristic approach provides a robust and efficient technique for parametric estimation of second-order transfer functions.
The integration of the Final Value Theorem offers substantial improvements in convergence speed and accuracy.
This generalized method offers a practical and adaptable solution for system identification across diverse engineering domains.