Optimized Adaptive Fuzzy Security Control of Nonlinear Systems With Prescribed Tracking Performance

Area of Science:

• Control Systems Engineering
• Artificial Intelligence
• Cybersecurity

Background:

• Nonlinear nonstrict-feedback systems are vulnerable to denial-of-service (DoS) attacks, compromising their performance.
• Immeasurable system states and unknown nonlinearities complicate control design under attack conditions.

Purpose of the Study:

• To develop an optimized fuzzy prescribed performance control strategy for nonlinear nonstrict-feedback systems under DoS attacks.
• To ensure robust tracking performance and minimize control resource consumption despite cyber threats.

Main Methods:

• Designed a fuzzy estimator to handle immeasurable states during DoS attacks.
• Constructed a prescribed performance error transformation and derived a Hamilton-Jacobi-Bellman equation for controller design.
• Integrated fuzzy-logic systems with reinforcement learning (RL) to approximate unknown nonlinearities.
• Proposed an optimized adaptive fuzzy security control law.

Main Results:

• Lyapunov stability analysis confirmed that tracking errors converge to a predefined region within finite time, even under DoS attacks.
• The RL-based algorithm effectively minimized the consumption of control resources.
• Simulations demonstrated the superior effectiveness of the proposed control algorithm compared to existing methods.

Conclusions:

• The proposed optimized adaptive fuzzy security control law effectively addresses control challenges in nonlinear systems under DoS attacks.
• The integration of fuzzy logic, RL, and prescribed performance control offers a robust solution for secure and efficient system operation.