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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.
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Dynamic dissipative control for fuzzy distributed parameter cyber physical system under input quantization and DoS

Jingzhao Chen¹, Liming Ding², Tengfei Li³

¹School of Electronic Information and Intelligent Manufacturing, SIAS University, Zhengzhou, Henan, China.

|October 3, 2024

Summary

This summary is machine-generated.

This study introduces finite-time dissipative control for nonlinear distributed parameter cyber-physical systems (DP-CPS). The method uses Takagi-Sugeno fuzzy models and dynamic quantizers to enhance network efficiency and robustness against attacks.

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Area of Science:

Control Systems Engineering
Cyber-Physical Systems
Nonlinear Dynamics

Background:

Distributed Parameter Cyber-Physical Systems (DP-CPS) present complex nonlinear dynamics.
Network resource optimization and security are critical challenges in DP-CPS.
Finite-time control is essential for applications requiring rapid system response.

Purpose of the Study:

To develop a finite-time dissipative control strategy for nonlinear DP-CPS.
To address the impact of dynamic quantization on system performance.
To design a control system robust against Denial-of-Service (DoS) attacks.

Main Methods:

Utilizing Takagi-Sugeno (T-S) fuzzy models to represent system nonlinearities.
Formulating the DP-CPS as a fuzzy parabolic partial differential equation (PDE).
Employing Lyapunov functional analysis to ensure finite-time boundedness under quantization.

Main Results:

A dynamic state control strategy is proposed, accounting for quantization and DoS attacks.
Conditions for finite-time dissipative control design are derived.
Adjustment parameters for dynamic quantizers are determined for the fuzzy closed-loop system.

Conclusions:

The proposed dissipative control method effectively manages nonlinear DP-CPS within finite time.
Simulation results validate the effectiveness of the control strategy and quantizer design.
The approach offers a robust solution for networked control systems facing quantization and security threats.