Observer-based model predictive control for uncertain NCS subject to hybrid attacks via interval type-2 T-S fuzzy...

Area of Science:

• Control Engineering
• Cybersecurity
• Fuzzy Systems

Background:

• Networked control systems (NCS) are vulnerable to hybrid malicious attacks, including denial-of-service (DoS) and false data injection (FDI).
• These attacks degrade system performance by causing packet loss or modifying signals.
• Uncertainty in discrete-time nonlinear systems complicates control design.

Purpose of the Study:

• To develop a robust observer-based model predictive control (MPC) algorithm for uncertain discrete-time nonlinear NCS.
• To design a secure observer capable of resisting FDI attacks.
• To propose a fuzzy MPC algorithm that ensures controller gain calculation and recursive feasibility.

Main Methods:

• Utilizing interval type-2 Takagi-Sugeno (IT2 T-S) fuzzy theory for system representation.
• Designing a secure observer to mitigate the impact of FDI attacks.
• Implementing an MPC algorithm that updates estimation error bounds to guarantee recursive feasibility.

Main Results:

• A novel observer-based MPC scheme was developed for NCS under hybrid attacks.
• The proposed observer effectively resists FDI attacks.
• The fuzzy MPC algorithm ensures recursive feasibility by updating estimation error bounds.

Conclusions:

• The developed observer-based MPC scheme provides a robust solution for uncertain nonlinear NCS facing hybrid malicious attacks.
• The integration of IT2 T-S fuzzy theory enhances the system's resilience.
• Illustrative examples confirm the effectiveness of the proposed security scheme.