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Related Concept Videos

Control Systems01:10

Control Systems

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...
Feedback control systems01:26

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...
Linear Approximation in Frequency Domain01:26

Linear Approximation in Frequency Domain

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.
Controller Configurations01:22

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

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...
Load-frequency control01:28

Load-frequency control

Load-frequency control (LFC) is vital for maintaining power system stability, ensuring that frequency and power flows remain within acceptable limits during load changes. Turbine-governor control eliminates rotor accelerations and decelerations following load changes. However, a steady-state frequency error persists when the change in the turbine-governor reference setting is zero. In an interconnected power system, each area agrees to export or import a scheduled amount of power through...

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Updated: Jul 7, 2026

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

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

Jiahua Ma1, Zhikai Yao2, Wenxiang Deng1

  • 1School of Mechanical Engineering, Nanjing University of Science and Technology, Nanjing, 210094, Jiangsu, China.

Neural Networks : the Official Journal of the International Neural Network Society
|May 16, 2025
PubMed
Summary
This summary is machine-generated.

This study introduces a novel fixed-time adaptive neural network control method to enhance the performance of nonlinear systems with uncertainties. The approach ensures fixed-time stability, overcoming control limitations in complex systems.

Keywords:
AdaptationFixed-time controlNeural networkUncertain nonlinear systems

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

  • Control Systems Engineering
  • Nonlinear Dynamics
  • Artificial Intelligence in Control

Background:

  • Uncertainties in nonlinear systems hinder control performance.
  • High-order nonlinear systems present significant control challenges.
  • Existing methods may suffer from conservatism or differential explosion.

Purpose of the Study:

  • To develop a fixed-time adaptive neural network compensation control method.
  • To address both uncertain nonlinearities and parametric uncertainties.
  • To improve control performance and ensure fixed-time stability.

Main Methods:

  • Designed a fixed-time adaptive neural network (FTANN) for uncertain nonlinearities.
  • Developed a new fixed-time adaptive law for parametric uncertainties.
  • Integrated FTANN and adaptive law with a gain-adaptive fixed-time filter within the dynamic surface control (DSC) framework.

Main Results:

  • The proposed controller guarantees fixed-time stability for all system states, as proven by Lyapunov analysis.
  • The method resolves the "differential explosion" problem inherent in some control designs.
  • Reduced controller conservatism by lowering the robust feedback gain.

Conclusions:

  • The novel fixed-time adaptive neural network control method effectively manages uncertainties in high-order nonlinear systems.
  • The integrated approach ensures system stability within a fixed time.
  • Simulation and experimental results validate the controller's superior performance.