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

PD Controller: Design01:26

PD Controller: Design

744
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,...
744
PI Controller: Design01:24

PI Controller: Design

1.5K
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...
1.5K
Controller Configurations01:22

Controller Configurations

457
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...
457
Time-Domain Interpretation of PD Control01:07

Time-Domain Interpretation of PD Control

474
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...
474
Time and frequency -Domain Interpretation of PI Control01:27

Time and frequency -Domain Interpretation of PI Control

492
Proportional-Integral (PI) controllers are essential in many control systems to improve stability and performance. They are commonly used in everyday devices like thermostats to enhance system damping and reduce steady-state error. When the zero in the controller's transfer function is optimally placed, the system benefits significantly in terms of stability and accuracy.
Acting as a low-pass filter, the PI controller slows the system's response and extends settling times. This requires...
492
Feedback control systems01:26

Feedback control systems

815
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...
815

You might also read

Related Articles

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

Sort by
Same author

Anti-Disturbance Intermediate Observer-Based Fault Estimation and Fault-Tolerant Control for Markovian Jump Systems.

IEEE transactions on cybernetics·2026
Same author

A novel dynamic signal Lemma for predefined-time stabilization of high-order nonlinear systems with dynamic uncertainties.

Neural networks : the official journal of the International Neural Network Society·2026
Same author

Observer-Based Fuzzy Secure Control for High-Order MASs Against Communication Delays Under Jointly Connected Switching Topology.

IEEE transactions on cybernetics·2026
Same author

Optimal Containment Control for Stochastic Multiagent Systems via Simplified ADP Under Secure Communication.

IEEE transactions on cybernetics·2026
Same author

Constraint-Based Fuzzy Adaptive Security Formation Control for Nonlinear Multiagent Systems Against Deception Attacks.

IEEE transactions on cybernetics·2025
Same author

Adaptive Bipartite Hybrid Event-Triggered Output Consensus of Heterogeneous Uncertain Multiagent Systems Under Fixed and Switching Topologies.

IEEE transactions on cybernetics·2025
Same journal

Optimization of mode discerning control for nonlinear hybrid systems subject to unknown inputs with applications to active fault diagnosis.

ISA transactions·2026
Same journal

Convergence evaluation of optimization-based stochastic iterative learning control.

ISA transactions·2026
Same journal

Adaptive utility-aware event-triggered reinforcement learning for hybrid attack scheduling against remote state estimation.

ISA transactions·2026
Same journal

Hybrid vehicle state estimation using closed-form liquid neural networks and nonlinear Kalman filtering.

ISA transactions·2026
Same journal

Cross-coupled synchronization control strategy for rebar binding robots based on impedance control.

ISA transactions·2026
Same journal

Gas flow tracking for electronic pressure control system in gas chromatography under state constraints and hysteresis：An innovative fuzzy adaptive control approach.

ISA transactions·2026
See all related articles

Related Experiment Video

Updated: Apr 17, 2026

Design and Application of a Fault Detection Method Based on Adaptive Filters and Rotational Speed Estimation for an Electro-Hydrostatic Actuator
06:45

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

2.2K

Model-free optimal controller design for continuous-time nonlinear systems by adaptive dynamic programming based on a

Jilie Zhang1, Huaguang Zhang2, Zhenwei Liu2

  • 1School of Information Science and Engineering, Northeastern University, Shenyang, Liaoning, 110819, PR China; School of Information Science and Technology, Southwest Jiaotong University, Chengdu, 610031, PR China.

ISA Transactions
|February 24, 2015
PubMed
Summary
This summary is machine-generated.

This study introduces novel online control schemes for unknown nonlinear systems using adaptive dynamic programming (ADP). These methods synthesize controllers without system models, enhancing control system design.

Keywords:
Adaptive dynamic programmingModel-free controllerOptimal controlPrecompensator

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

14.4K
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

9.2K

Related Experiment Videos

Last Updated: Apr 17, 2026

Design and Application of a Fault Detection Method Based on Adaptive Filters and Rotational Speed Estimation for an Electro-Hydrostatic Actuator
06:45

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

2.2K
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

14.4K
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

9.2K

Area of Science:

  • Control Theory
  • Nonlinear Systems
  • Machine Learning

Background:

  • Developing controllers for unknown nonlinear systems is challenging.
  • Model-free control strategies are highly desirable in many applications.
  • Adaptive Dynamic Programming (ADP) offers a data-driven approach to control.

Purpose of the Study:

  • To present two new online schemes for synthesizing controllers for continuous-time nonlinear systems with unknown dynamics.
  • To develop a model-free control approach using adaptive dynamic programming.
  • To circumvent the need for prior system knowledge through a precompensator.

Main Methods:

  • Utilizing two new implementation schemes based on adaptive dynamic programming (ADP).
  • Introducing a precompensator to construct an augmented system, bypassing the need for system identification.
  • Solving the Hamilton-Jacobi-Bellman (HJB) equation using ADP, incorporating least-squared technique, neural network approximation, and policy iteration (PI).
  • Updating neural network weights by sampling state, state derivative, and input information within the PI framework.

Main Results:

  • Demonstrated the effectiveness of the proposed online control schemes through several examples.
  • Successfully synthesized controllers for unknown nonlinear systems without explicit system models.
  • Validated the integration of least-squared technique, neural network approximator, and policy iteration for ADP-based control.

Conclusions:

  • The presented adaptive dynamic programming schemes provide a viable approach for model-free control of unknown nonlinear systems.
  • The use of a precompensator effectively addresses the challenge of unknown system dynamics.
  • The proposed methods offer a practical and effective solution for real-world control problems where system models are unavailable.