Sliding Mode Control for Sampled-Data Systems Subject to Deception Attacks: Handling Randomly Perturbed Sampling...

Area of Science:

• Control Systems Engineering
• Cybersecurity
• Stochastic Systems

Background:

• Sampled-data systems are susceptible to cyberattacks, particularly deception attacks that compromise data integrity.
• Random variations in sampling periods and communication channel vulnerabilities introduce significant challenges in control system design.
• Markovian chains and Bernoulli stochastic variables are essential tools for modeling these uncertainties.

Purpose of the Study:

• To design a robust sliding mode controller for sampled-data systems under deception attacks.
• To analyze system stability considering random sampling perturbations and stochastic attacks.
• To optimize control performance for enhanced system reliability.

Main Methods:

• Sliding mode control theory is applied to design a controller for the specified system class.
• Sufficient conditions for exponential ultimate boundedness in the mean-square sense are derived.
• An optimization problem is formulated to achieve locally optimal control performance.

Main Results:

• The proposed sliding mode controller guarantees the exponentially ultimate boundedness of the closed-loop system.
• The derived conditions effectively address the challenges posed by random sampling and deception attacks.
• Simulation results validate the controller's effectiveness and performance advantages.

Conclusions:

• The developed sliding mode control strategy provides a robust solution for sampled-data systems facing deception attacks.
• The approach effectively handles random sampling periods and stochastic communication channel vulnerabilities.
• The study offers a valuable framework for designing secure and reliable control systems in adversarial environments.