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Feedback control systems01:26

Feedback control systems

314
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...
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Control Systems01:10

Control Systems

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

Time-Domain Interpretation of PD Control

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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...
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Sampling Theorem01:15

Sampling Theorem

340
In signal processing, the analysis of continuous-time signals, denoted as x(t), often involves sampling techniques to convert these signals into discrete-time signals. This process is essential for digital representation and manipulation. A critical component in sampling is the train of impulses, characterized by the sampling interval and the sampling frequency. The relationship between these parameters and the original signal's properties dictates the success of the sampling process.
340
Sampling Plans01:23

Sampling Plans

181
Sampling is a crucial step in analytical chemistry, allowing researchers to collect representative data from a large population. Common sampling methods include random, judgmental, systematic, stratified, and cluster sampling.
Random sampling is a method where each member of the population has an equal chance of being selected for the sample. It involves selecting individuals randomly, often using random number generators or lottery-type methods. For example, when analyzing the properties of a...
181
Upsampling01:22

Upsampling

237
Managing signal sampling rates is essential in digital signal processing to maintain signal integrity. A decimated signal, characterized by a reduced frequency range due to its lower sampling rate, can be upsampled by inserting zeros between each sample. This upsampling process expands the original spectrum and introduces repeated spectral replicas at intervals dictated by the new Nyquist frequency. To refine this zero-inserted sequence, it is passed through a lowpass filter with a cutoff...
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A Sampled-Data-Based Secure Control Approach for Networked Control Systems Under Random DoS Attacks.

Jiancun Wu, Chen Peng, Jin Zhang

    IEEE Transactions on Cybernetics
    |January 25, 2024
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    Summary

    This study enhances networked control system (NCS) security against denial of service (DoS) attacks. A new controller uses attack probability and packet dropout data for improved stability.

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    Area of Science:

    • Control Engineering
    • Networked Control Systems (NCSs)
    • Cybersecurity in Control Systems

    Background:

    • Networked control systems (NCSs) are vulnerable to random denial of service (DoS) attacks, compromising stability.
    • Existing methods for analyzing random attacks lack the ability to capture maximum attack duration.
    • Understanding attack characteristics is crucial for designing robust security controllers in NCSs.

    Purpose of the Study:

    • To analyze H∞ stability of NCSs subjected to random DoS attacks.
    • To design a sampled-data-based state feedback security controller to counteract DoS attack influences.
    • To develop a framework that integrates random DoS attacks and time-varying delays within a unified model.

    Main Methods:

    • Introduction of a logical processor to capture the maximum duration of DoS attacks.
    • Utilizing periodic sampling techniques to calculate attack occurrence probability and maximum allowable packet dropouts.
    • Development of a novel networked sampled-data system model and a DoS-dependent security controller.
    • Employing a Lyapunov-Krasovskii functional to establish the relationship between stability and attack characteristics.

    Main Results:

    • A new method for analyzing NCS stability under DoS attacks by quantifying attack duration and probability.
    • Design of a state feedback controller that effectively utilizes attack information to mitigate instability.
    • Demonstration of mean square asymptotic stability through a Lyapunov-Krasovskii functional approach.
    • Validation of the control strategy's effectiveness in practical engineering applications.

    Conclusions:

    • The proposed sampled-data-based security controller effectively enhances the H∞ stability of NCSs under random DoS attacks.
    • The developed framework provides a unified approach to handle both random DoS attacks and associated time-varying delays.
    • The findings offer a reliable and applicable control strategy for securing NCSs against cyber-physical threats.