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

Linear Approximation in Time Domain

Linear Approximation in Time Domain

Nonlinear systems often require sophisticated approaches for accurate modeling and analysis, with state-space representation being particularly effective. This method is especially useful for systems where variables and parameters vary with time or operating conditions, such as in a simple pendulum or a translational mechanical system with nonlinear springs.
For a simple pendulum with a mass evenly distributed along its length and the center of mass located at half the pendulum's length, the...

Linear Approximation in Frequency Domain

Linear Approximation in Frequency Domain

Linear systems are characterized by two main properties: superposition and homogeneity. Superposition allows the response to multiple inputs to be the sum of the responses to each individual input. Homogeneity ensures that scaling an input by a scalar results in the response being scaled by the same scalar.
In contrast, nonlinear systems do not inherently possess these properties. However, for small deviations around an operating point, a nonlinear system can often be approximated as linear.

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 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...

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...

You might also read

Related Articles

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

Sort by

Same author

The production of recombinant proteins in transgenic barley grains.

Proceedings of the National Academy of Sciences of the United States of America·2000

Same author

Vaccinia as a vector for tumor-directed gene therapy: biodistribution of a thymidine kinase-deleted mutant.

Cancer gene therapy·2000

Same author

Bovine NAD+-dependent isocitrate dehydrogenase: alternative splicing and tissue-dependent expression of subunit 1.

Biochemistry·2000

Same author

Determination of trace elements in tissue of human uterine cancer by instrumental neutron activation analysis.

Biological trace element research·2000

Same author

Toluene diisocyanate enhances substance P in sensory neurons innervating the nasal mucosa.

American journal of respiratory and critical care medicine·2000

Same author

Asbestos in extrapulmonary sites: omentum and mesentery.

Chest·2000

Same journal

Universal perceptron and DNA-like learning algorithm for binary neural networks: LSBF and PBF implementations.

IEEE transactions on neural networks·2013

Same journal

Guest editorial: special section on white box nonlinear prediction models.

IEEE transactions on neural networks·2011

Same journal

Data-based fault-tolerant control of high-speed trains with traction/braking notch nonlinearities and actuator failures.

IEEE transactions on neural networks·2011

Same journal

Guest editorial: special section on data-based control, modeling, and optimization.

IEEE transactions on neural networks·2011

Same journal

Neural network-based multiple robot simultaneous localization and mapping.

IEEE transactions on neural networks·2011

Same journal

Data-driven model-free adaptive control for a class of MIMO nonlinear discrete-time systems.

IEEE transactions on neural networks·2011

See all related articles

Search research articles

Related Experiment Video

Updated: Jul 7, 2026

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

A neural-network method for the nonlinear servomechanism problem.

Y C Chu¹, J Huang

¹School of Electrical and Electronic Engineering, Nanyang Technological University, Singapore.

IEEE Transactions on Neural Networks

|February 7, 2008

Summary

This summary is machine-generated.

This study introduces a novel recurrent neural network approach to solve complex nonlinear servomechanism problems. This method offers an effective way to find approximate solutions for the challenging regulator equations.

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

Related Experiment Videos

Last Updated: Jul 7, 2026

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

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

Area of Science:

Control Systems Engineering
Computational Mathematics
Artificial Intelligence

Background:

Nonlinear servomechanism control requires solving complex regulator equations, which are difficult to solve exactly due to their nonlinear nature.
Existing methods struggle to provide precise solutions for these nonlinear partial differential and algebraic equations.

Purpose of the Study:

To propose an effective computational method for solving the nonlinear servomechanism problem.
To apply a specific class of recurrent neural networks to address the challenges posed by the regulator equations.

Main Methods:

Utilizing a recurrent neural network with cellular neural network characteristics to approximate the solution of the regulator equations.
Developing a novel design methodology for nonlinear servomechanisms based on neural network computation.

Main Results:

Demonstrated the feasibility of using recurrent neural networks for numerical solutions in control systems.
Achieved an effective approximate solution for the nonlinear servomechanism problem.

Conclusions:

The proposed neural network-based approach provides an effective method for tackling nonlinear servomechanism problems.
This research highlights a significant application of neural networks in numerical mathematics and control engineering, illustrated by the ball and beam system example.