Sliding Mode Secure Control for Markov Jump Systems: Dealing With Random Nonuniform Sampling Issues.

Area of Science:

• Control Theory
• Systems Engineering
• Network Security

Background:

• Markov jump systems (MJSs) exhibit state transitions governed by a Markov chain.
• Networked control systems are vulnerable to random sampling and deception attacks.
• Existing control strategies may not adequately address combined stochastic uncertainties.

Purpose of the Study:

• To develop a sliding mode control (SMC) strategy for MJSs with random sampling and deception attacks.
• To design a mode detection scheme for simultaneously identifying system and attack modes.
• To ensure the stability and boundedness of the closed-loop system under uncertain conditions.

Main Methods:

• Mapping three Markov chains into a single one for simplified analysis.
• Developing a mode detection algorithm for partially inaccessible system and attack modes.
• Designing a detected-mode-dependent SMC controller to handle stochastic dynamics.

Main Results:

• The proposed controller guarantees reachability of the sliding surface.
• Mean-square exponential ultimate boundedness of the closed-loop system is achieved.
• Simulation examples validate the effectiveness of the control method.

Conclusions:

• The developed SMC approach effectively manages MJSs with random sampling and deception attacks.
• The mode detection scheme enhances system resilience against network vulnerabilities.
• The findings contribute to robust control design for uncertain networked systems.