Cyber-physical system parameter-unknown intrusion control method and device under malicious attack
By designing a fixed-time neural adaptive controller, the control problem of nonlinear cyber-physical systems with unknown parameters under malicious attacks is solved. The system state error is converged within a fixed time, reducing the accuracy requirements of the system model and making it suitable for the security control of various cyber-physical systems.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Patents(China)
- Current Assignee / Owner
- UNIV OF SCI & TECH BEIJING
- Filing Date
- 2023-12-29
- Publication Date
- 2026-07-24
AI Technical Summary
Existing technologies cannot effectively solve the fixed-time control problem of nonlinear cyber-physical systems with unknown parameters under malicious attacks, cannot meet the system convergence time requirements, and have high requirements for system models and attack perception, and cannot handle the stability of systems whose initial states exceed the limits.
A virtual controller is designed using the mathematical model of a high-order nonlinear cyber-physical system, error transfer function, coordinate transformation, backstepping method, and obstacle Lyapunov method. By combining Gaussian radial basis function neural network and parameter adaptive method, unknown nonlinear terms and malicious attacks are fitted to design a fixed-time neural adaptive controller.
It achieves system state error convergence within a fixed time for high-order nonlinear cyber-physical systems with unknown parameters under malicious attacks, reduces the accuracy requirements of the system model, and can handle error convergence under arbitrary initial states, making it suitable for security control of various cyber-physical systems.
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Figure CN117792773B_ABST