Adaptive Nussbaum Design for Nonholonomic Systems With Asymptotic Stabilization Against False Data Injection
提出一种自适应Nussbaum控制策略，应对虚假数据注入攻击对非完整系统状态信息的破坏，通过在线学习和模糊逻辑补偿未知非线性，实现渐近镇定并保证信号有界。
This article addresses the stabilization challenges of nonholonomic systems under the threat of false data injection (FDI) attacks, which compromise the integrity of state information. A novel adaptive control strategy using Nussbaum-type gains is proposed to ensure the asymptotic stability of the closed-loop system while maintaining signal boundedness. The approach extends conventional Nussbaum designs to handle multiple unknown control directions. It integrates online learning mechanisms to mitigate the impact of FDI attacks. Additionally, adaptive backstepping and fuzzy-logic systems are utilized to approximate and compensate for unknown nonlinear dynamics. The methodology transforms nonholonomic systems into equivalent cascade structures to address inherent constraints and enable secure control input design. Simulation studies validate the effectiveness and resilience of the proposed control strategy, demonstrating significant improvements in stability and robustness in the presence of FDI attacks.