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

State Space Representation

State Space Representation

The frequency-domain technique, commonly used in analyzing and designing feedback control systems, is effective for linear, time-invariant systems. However, it falls short when dealing with nonlinear, time-varying, and multiple-input multiple-output systems. The time-domain or state-space approach addresses these limitations by utilizing state variables to construct simultaneous, first-order differential equations, known as state equations, for an nth-order system.
Consider an RLC circuit, a...

You might also read

Related Articles

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

Sort by

Same author

Impulsive Neural Control to Schedule Antivirals and Immunomodulators for COVID-19.

Proceedings of the ... IEEE Conference on Decision & Control. IEEE Conference on Decision & Control·2023

Same author

Mechanical Performance of 3D-Printed Biocompatible Polycarbonate for Biomechanical Applications.

Polymers·2021

Same author

A Comparative Analysis of Chemical, Thermal, and Mechanical Post-Process of Fused Filament Fabricated Polyetherimide Parts for Surface Quality Enhancement.

Materials (Basel, Switzerland)·2021

Same author

Treatment for T1DM patients by a neuro-fuzzy inverse optimal controller including multi-step prediction.

ISA transactions·2021

Same author

Experimental, Computational, and Dimensional Analysis of the Mechanical Performance of Fused Filament Fabrication Parts.

Polymers·2021

Same author

Adaptive neural PD controllers for mobile manipulator trajectory tracking.

PeerJ. Computer science·2021

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: Jun 5, 2026

Brain State-dependent Brain Stimulation with Real-time Electroencephalography-Triggered Transcranial Magnetic Stimulation

Brain State-dependent Brain Stimulation with Real-time Electroencephalography-Triggered Transcranial Magnetic Stimulation

Published on: August 20, 2019

Real-time recurrent neural state estimation.

Alma Y Alanis¹, Edgar N Sanchez, Alexander G Loukianov

¹Centro Universitario de Ciencias Exactas e Ingenierías, Universidad de Guadalajara, Jalisco 44430, Mexico. almayalanis@gmail.com

IEEE Transactions on Neural Networks

|January 20, 2011

Summary

This summary is machine-generated.

This study introduces a novel neural observer for nonlinear systems, effectively handling disturbances and uncertainties. Real-time motor control demonstrates its practical application in engineering.

More Related Videos

A Method for Tracking the Time Evolution of Steady-State Evoked Potentials

A Method for Tracking the Time Evolution of Steady-State Evoked Potentials

Published on: May 25, 2019

Related Experiment Videos

Last Updated: Jun 5, 2026

Brain State-dependent Brain Stimulation with Real-time Electroencephalography-Triggered Transcranial Magnetic Stimulation

Brain State-dependent Brain Stimulation with Real-time Electroencephalography-Triggered Transcranial Magnetic Stimulation

Published on: August 20, 2019

A Method for Tracking the Time Evolution of Steady-State Evoked Potentials

A Method for Tracking the Time Evolution of Steady-State Evoked Potentials

Published on: May 25, 2019

Area of Science:

Control Systems Engineering
Artificial Neural Networks
Nonlinear System Analysis

Background:

Unknown nonlinear systems present challenges in control and estimation.
External disturbances and parameter uncertainties degrade observer performance.
Discrete-time systems require specialized observer designs.

Purpose of the Study:

To develop a robust nonlinear discrete-time neural observer.
To address external disturbances and parameter uncertainties.
To validate the observer through real-time implementation.

Main Methods:

Utilizing a discrete-time recurrent high-order neural network.
Employing an extended Kalman filter-based training algorithm.
Proving system stability via the Lyapunov approach.

Main Results:

The proposed neural observer accurately estimates system states despite disturbances and uncertainties.
Stability of the observer is rigorously proven.
Successful real-time implementation on a three-phase induction motor.

Conclusions:

The developed neural observer offers a robust solution for discrete-time nonlinear systems.
The extended Kalman filter training enhances observer performance and stability.
The scheme is applicable to real-world engineering problems like motor control.