Fault Detection of Unmanned Surface Vehicles: The Fuzzy Multiprocessor Implementation

Abstract

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.