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

Controller Configurations01:22

Controller Configurations

333
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...
333
Time-Domain Interpretation of PD Control01:07

Time-Domain Interpretation of PD Control

348
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...
348
Feedback control systems01:26

Feedback control systems

657
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...
657
Control Systems01:10

Control Systems

1.8K
Control systems are everywhere in contemporary society, influencing diverse applications from aerospace to automated manufacturing. These systems can be found naturally within biological processes, such as blood sugar regulation and heart rate adjustment in response to stress, as well as in man-made systems like elevators and automated vehicles. A control system is essentially a network of subsystems and processes that collaboratively convert specific inputs into desired outputs.
At the heart...
1.8K
State Space Representation01:27

State Space Representation

499
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...
499
Control Systems: Applications01:25

Control Systems: Applications

1.1K
Electrical engineering plays a pivotal role in our daily lives, with control systems at the heart of many applications, from home appliances to sophisticated space shuttles. Control systems manage and regulate the behavior of devices and processes, ensuring they function safely, correctly, and efficiently.
In modern vehicles, control systems manage various functions to enhance performance and safety. The steering wheel and accelerator are primary inputs in a car's control system. The...
1.1K

You might also read

Related Articles

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

Sort by
Same author

Output feedback-based adaptive position control for hydraulic systems with preset performance.

ISA transactions·2026
Same author

Physics-Informed Monotonic Conformer for Remaining Useful Life Prediction of Hydraulic Systems.

Sensors (Basel, Switzerland)·2026
Same author

Explainable Dynamic Graph Learning and Multi-Scale Feature Fusion for Hydraulic System Health Monitoring.

Sensors (Basel, Switzerland)·2026
Same author

Flow pulsation compensation based composite adaptive active disturbance rejection control for electro-hydrostatic actuators.

ISA transactions·2025
Same author

Fixed-time adaptive neural network compensation control for uncertain nonlinear systems.

Neural networks : the official journal of the International Neural Network Society·2025
Same author

Genipin improves obesity through promoting bile secretion and changing bile acids composition in diet-induced obese rats.

The Journal of pharmacy and pharmacology·2024
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
Same journal

Stackelberg differential game-based fuzzy adaptive hierarchical optimal control for a nonlinear system with unknown dynamics.

ISA transactions·2026
Same journal

Composite fault-tolerant predictive control strategy for PMSM demagnetization faults.

ISA transactions·2026
Same journal

Bias-compensated Q-learning for optimal tracking control under denial-of-service attacks.

ISA transactions·2026
See all related articles

Related Experiment Video

Updated: Jan 8, 2026

Real-Time Proxy-Control of Re-Parameterized Peripheral Signals using a Close-Loop Interface
11:54

Real-Time Proxy-Control of Re-Parameterized Peripheral Signals using a Close-Loop Interface

Published on: May 8, 2021

5.0K

Neuroadaptive fixed-time prescribed performance for full-state-constrained uncertain systems using dynamic surface

Zhangbao Xu1, Maokun Zhang2, Jianyong Yao3

  • 1School of Computer and Information Engineering, Anhui Engineering Research Center for Intelligent Computing and Information Innovation, Fuyang Normal University, Fuyang 236037, China.

ISA Transactions
|December 18, 2025
PubMed
Summary
This summary is machine-generated.

This study introduces a novel neuroadaptive controller for systems with unknown dynamics and constraints. The controller ensures fast and accurate tracking performance while respecting system limitations.

Keywords:
Adaptive controlFixed-time prescribed performance controlFull-state constraintsNeural networkUncertain systems

More Related Videos

WheelCon: A Wheel Control-Based Gaming Platform for Studying Human Sensorimotor Control
08:18

WheelCon: A Wheel Control-Based Gaming Platform for Studying Human Sensorimotor Control

Published on: August 15, 2020

5.4K
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

14.2K

Related Experiment Videos

Last Updated: Jan 8, 2026

Real-Time Proxy-Control of Re-Parameterized Peripheral Signals using a Close-Loop Interface
11:54

Real-Time Proxy-Control of Re-Parameterized Peripheral Signals using a Close-Loop Interface

Published on: May 8, 2021

5.0K
WheelCon: A Wheel Control-Based Gaming Platform for Studying Human Sensorimotor Control
08:18

WheelCon: A Wheel Control-Based Gaming Platform for Studying Human Sensorimotor Control

Published on: August 15, 2020

5.4K
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

14.2K

Area of Science:

  • Control Systems Engineering
  • Adaptive Control Theory
  • Nonlinear System Analysis

Background:

  • Controlling complex systems with uncertainties and full-state constraints is challenging.
  • Existing methods often struggle with unknown dynamics and disturbances, requiring complex transformations.
  • The need for robust and efficient control strategies that guarantee performance within fixed timeframes is critical.

Purpose of the Study:

  • To develop a precise control strategy for full-state constrained systems with uncertainties and unknown dynamics.
  • To design a neuroadaptive controller that compensates for unknown disturbances and parametric uncertainties.
  • To ensure tracking error convergence within a fixed time while adhering to all state constraints.

Main Methods:

  • A neuroadaptive strategy is employed to handle unknown system dynamics and uncertainties.
  • Disturbance observers are utilized for estimating unknown external disturbances.
  • A novel Lyapunov function with an asymmetric prescribed performance function is constructed for fixed-time convergence.
  • Dynamic surface technology is integrated to mitigate the differential explosion issue in backstepping design.

Main Results:

  • A neuroadaptive fixed-time prescribed performance controller is successfully developed.
  • The controller simplifies design by avoiding prior tracking error transformation functions.
  • Lyapunov theory confirms that the error system is locally ultimately exponentially bounded.
  • Asymmetric fixed-time prescribed tracking performance is achieved without violating state constraints.

Conclusions:

  • The proposed neuroadaptive controller effectively manages full-state constrained systems with uncertainties and unknown dynamics.
  • The novel approach guarantees fixed-time convergence and respects system constraints, offering a simplified design.
  • Experimental validation confirms the controller's practical applicability and performance.