Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

Linear Approximation in Time Domain01:21

Linear Approximation in Time Domain

130
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,...
130
Feedback control systems01:26

Feedback control systems

440
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...
440
Linear Approximation in Frequency Domain01:26

Linear Approximation in Frequency Domain

139
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....
139
PD Controller: Design01:26

PD Controller: Design

359
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,...
359
PI Controller: Design01:24

PI Controller: Design

514
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...
514
Open and closed-loop control systems01:17

Open and closed-loop control systems

1.0K
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...
1.0K

You might also read

Related Articles

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

Sort by
Same author

Distributed Security and Safety-Critical Formation Control for Multirobot Systems Subject to Distributed Denial-of-Service Attacks.

IEEE transactions on cybernetics·2026
Same author

A Stochastic Hybrid Approach to Decentralized Networked Control: Stochastic Network Delays and Poisson Pulsing Attacks.

IEEE transactions on cybernetics·2026
Same author

Active disturbance rejection control synthesis for industrial time-delayed process: an observation reconfiguration perspective.

ISA transactions·2026
Same author

Increasing Soil Organic Carbon but Decoupling of Ecological Attributes After Loss of Dominant Functional Groups in Alpine Meadow.

Ecology and evolution·2025
Same author

Drug dosing for cancer therapy: A stochastic model predictive control perspective.

Journal of theoretical biology·2025
Same author

Memory-Efficient Inverse Reinforcement Learning for Multiplayer Differential Games.

IEEE transactions on cybernetics·2025
Same journal

A robust ATUB-Net for bearing fault diagnosis under unbalanced sample scenarios.

ISA transactions·2026
Same journal

Data-driven trajectory tracking control of UAV systems under a novel probability-selection event-triggered mechanism.

ISA transactions·2026
Same journal

Predefined-time affine formation tracking control of unmanned surface vehicles with input saturation via adaptive fuzzy observers.

ISA transactions·2026
Same journal

Adaptive fault-tolerant safety-guaranteed fuzzy event-triggered rendezvous control for heterogeneous USV-UUV systems.

ISA transactions·2026
Same journal

Two-stage maximum likelihood weighted recursive least squares algorithm for nonlinear systems and an application in wind tunnel systems.

ISA transactions·2026
Same journal

Enhancing interpretable soft sensing with embedded hybrid modeling: the GraphTrans approach for industrial processes.

ISA transactions·2026
See all related articles

Related Experiment Video

Updated: Sep 18, 2025

Gain-compensation Methodology for a Sinusoidal Scan of a Galvanometer Mirror in Proportional-Integral-Differential Control Using Pre-emphasis Techniques
09:01

Gain-compensation Methodology for a Sinusoidal Scan of a Galvanometer Mirror in Proportional-Integral-Differential Control Using Pre-emphasis Techniques

Published on: April 4, 2017

8.8K

Data-driven parallel linear controllers for reference tracking in nonlinear systems.

Yao Shi1, José M Maestre2, Lei Xie1

  • 1State Key Laboratory of Industrial Control Technology, Zhejiang University, 310027 Hangzhou, China.

ISA Transactions
|June 25, 2025
PubMed
Summary
This summary is machine-generated.

This study introduces parallel linear controllers (PLIC), a novel data-driven method for nonlinear system control without models. PLIC effectively achieves reference tracking by combining inverse control and error compensation strategies.

Keywords:
Closed-loop stabilityData-driven controlParallel controllersReference tracking

More Related Videos

An Experimental Platform to Study the Closed-loop Performance of Brain-machine Interfaces
10:51

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

Published on: March 10, 2011

13.8K
A Protocol for Real-time 3D Single Particle Tracking
10:16

A Protocol for Real-time 3D Single Particle Tracking

Published on: January 3, 2018

15.0K

Related Experiment Videos

Last Updated: Sep 18, 2025

Gain-compensation Methodology for a Sinusoidal Scan of a Galvanometer Mirror in Proportional-Integral-Differential Control Using Pre-emphasis Techniques
09:01

Gain-compensation Methodology for a Sinusoidal Scan of a Galvanometer Mirror in Proportional-Integral-Differential Control Using Pre-emphasis Techniques

Published on: April 4, 2017

8.8K
An Experimental Platform to Study the Closed-loop Performance of Brain-machine Interfaces
10:51

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

Published on: March 10, 2011

13.8K
A Protocol for Real-time 3D Single Particle Tracking
10:16

A Protocol for Real-time 3D Single Particle Tracking

Published on: January 3, 2018

15.0K

Area of Science:

  • Control Theory
  • Nonlinear Systems
  • Data-Driven Methods

Background:

  • Nonlinear system control is challenging, especially without models and with real-time computation needs.
  • Existing methods often require accurate system models, limiting their applicability.

Purpose of the Study:

  • To develop a purely data-driven approach for reference tracking control in nonlinear systems.
  • To address challenges of model unavailability and real-time computation requirements.

Main Methods:

  • Proposed a parallel linear controllers (PLIC) architecture with two concurrent linear controllers.
  • Employed Koopman operator for system lifting and quadratic programming for constraint handling in one controller.
  • Utilized a modified direct data-driven virtual reference tuning for error compensation in the second controller.

Main Results:

  • Analyzed the closed-loop properties of the proposed PLIC method.
  • Demonstrated the efficacy of PLIC through benchmark simulations.
  • Achieved effective reference tracking control using only data, without explicit system models.

Conclusions:

  • The parallel linear controllers (PLIC) offer a viable data-driven solution for reference tracking in nonlinear systems.
  • The method successfully handles model unavailability and real-time constraints.
  • PLIC shows promise for practical applications in complex control scenarios.