Adaptive Nussbaum Design for Nonholonomic Systems With Asymptotic Stabilization Against False Data Injection

Area of Science:

• Control Systems Engineering
• Cybersecurity
• Robotics

Background:

• Nonholonomic systems face significant stabilization challenges, especially when subjected to false data injection (FDI) attacks.
• FDI attacks compromise system state integrity, necessitating robust control solutions.

Purpose of the Study:

• To develop a novel adaptive control strategy for stabilizing nonholonomic systems under FDI attacks.
• To ensure asymptotic stability and signal boundedness of the closed-loop system despite adversarial conditions.

Main Methods:

• A Nussbaum-type adaptive control strategy with online learning mechanisms is proposed.
• Adaptive backstepping and fuzzy-logic systems are employed to approximate unknown nonlinear dynamics.
• Nonholonomic systems are transformed into equivalent cascade structures for secure control design.

Main Results:

• The proposed strategy effectively mitigates the impact of FDI attacks.
• Asymptotic stability and signal boundedness are achieved in the presence of uncertainties and attacks.
• Simulations demonstrate enhanced stability and robustness compared to conventional methods.

Conclusions:

• The novel adaptive control strategy offers a resilient solution for nonholonomic systems vulnerable to FDI attacks.
• The integration of Nussbaum gains, online learning, and fuzzy logic provides effective compensation for system uncertainties and cyber threats.