In this article, we study the fault detection problem of unmanned surface vehicles through the implementation of fuzzy multiprocessors. By employing the Takagi–Sugeno fuzzy technique, the linear approximation of unmanned surface vehicles is obtained, and a fuzzy multiprocessor architecture is proposed to estimate the state of unmanned surface vehicles. With the residual signal generated by multiprocessors, a detection logic is designed to realize the fault detection. Based on the Lyapunov method, sufficient conditions are given to ensure that the error dynamic system is asymptotically stable and meets the given $H_{\infty }$ and $H\_$ performance. Assisted by genetic algorithms, a two-step optimization algorithm is proposed to optimize the mixed $H_{\infty }$ and $H\_$ performance. Finally, case studies are provided to verify the effectiveness and superiority of the proposed method.

中文翻译：

---

无人水面航行器的故障检测：模糊多处理器实现


在本文中，我们通过模糊多处理器的实现研究了无人水面航行器的故障检测问题。通过采用 Takagi–Sugeno 模糊技术，获得了无人水面航行器的线性近似，并提出了一种模糊多处理器架构来估计无人水面航行器的状态。利用多处理器产生的残余信号，设计检测逻辑实现故障检测。基于Lyapunov方法，给出了足够的条件来确保误差动态系统渐近稳定并满足给定的$H_{\infty }$和$H\_$性能。在遗传算法的辅助下，提出了一种两步优化算法来优化混合$H_{\infty }$和$H\_$的性能。最后，通过实例验证了所提方法的有效性和优越性。