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

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...

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,...

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...

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...

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,...

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...

You might also read

Related Articles

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

Sort by

Same author

Comparative studies on zirconia and graphene composites obtained by one-step and stepwise electrodeposition for deoxyribonucleic acid sensing.

Analytica chimica acta·2013

Same author

Genetic etiology study of the non-syndromic deafness in Chinese Hans by targeted next-generation sequencing.

Orphanet journal of rare diseases·2013

Same author

Observation of the sixth polymorph of BiB3O6: in situ high-pressure Raman spectroscopy and synchrotron X-ray diffraction studies on the β-polymorph.

Inorganic chemistry·2013

Same author

Identification of a linear B-cell epitope within the Bluetongue virus serotype 8 NS2 protein using a phage-displayed random peptide library.

Veterinary immunology and immunopathology·2013

Same author

MicroRNA-214 provokes cardiac hypertrophy via repression of EZH2.

Biochemical and biophysical research communications·2013

Same author

The prevalence of thyroid nodules and its relationship with metabolic parameters in a Chinese community-based population aged over 40 years.

Endocrine·2013

Search research articles

Related Experiment Videos

Synchronizing chaotic dynamics with uncertainties based on a sliding mode control design.

Tao Yang¹, Hui He Shao

¹Department of Automation, Shanghai Jiao Tong University, Shanghai 200030, China.

Physical Review. E, Statistical, Nonlinear, and Soft Matter Physics

|May 15, 2002

Summary

This study presents a new feedback controller for synchronizing uncertain chaotic systems. The method uses an extended state observer to manage uncertainties, enabling reliable synchronization of systems like Lorenz and Duffing oscillators.

Related Experiment Videos

Area of Science:

Control Theory
Nonlinear Dynamics
Chaos Theory

Background:

Chaotic systems often exhibit uncertainties like parameter mismatching.
Synchronizing uncertain chaotic systems is challenging but crucial for applications.
Existing methods may not effectively handle unmodeled dynamics or parameter variations.

Purpose of the Study:

To develop a robust feedback control strategy for synchronizing two continuous chaotic systems with uncertainties.
To design an extended state observer to estimate and compensate for system uncertainties.
To demonstrate the practical applicability of the proposed controller for chaotic system synchronization.

Main Methods:

Sliding mode control (SMC) design for feedback control.
Utilizing an extended state observer (ESO) to estimate system states and uncertainties.
Employing only the synchronization error for observer-based uncertainty compensation.
Realizing the feedback controller based on observer states.

Main Results:

The proposed observer-based sliding mode controller effectively synchronizes chaotic systems with uncertainties.
Demonstrated successful synchronization for Duffing, Van der Pol, and Lorenz chaotic systems.
The method compensates for parameter mismatching and structure differences.
The controller is physically realizable and robust to uncertainties.

Conclusions:

The developed observer-based sliding mode control approach provides an effective solution for synchronizing uncertain chaotic systems.
This method enhances the robustness and applicability of chaotic system synchronization.
The findings offer a valuable tool for researchers and engineers working with chaotic dynamics.