**5. Conclusions**

Thruster is the driving mechanism for AUV movement, whose fault diagnostics and fault tolerant control are the premise to complete the underwater missions. In practice, ocean currents perturbations could produce noise for thruster fault diagnosis, in this paper, the PFCM algorithm is proposed to solve the problem of thruster fault diagnostics. It is not enough just to realize the thruster fault diagnostics, in order to successfully complete the missions with thruster fault, a fuzzy controller is presented. Considering the effect of ocean currents, the CPSO algorithm is developed to optimize the fuzzy controller, which guarantees the fault tolerant control performance. Based on the designed AUV, a date set is obtained to demonstrate the effectiveness of the thruster fault diagnostics. Different scenarios of path tracking are given to illustrate the performance of the proposed algorithm. Compared with the traditional fuzzy fault tolerant control, the tracking path length and tracking error are smaller by the proposed algorithm, which illustrates the proposed algorithm. In this paper, the proposed algorithm is difficult to be used for weak faults diagnosis of AUV thrusters. However, major faults are generally developed from weak faults. Therefore, in future work, we will try to solve the problem of accurate weak faults diagnosis of AUV thrusters in the presence of interference, which is one of the keys to preventing and reducing catastrophic accidents.

**Author Contributions:** Conceptualization, Q.T. and T.W.; methodology, Q.T.; software, T.W.; validation, Q.T., G.R. and B.L.; formal analysis, Q.T.; investigation, Q.T. and T.W.; resources, G.R.; data curation, T.W.; writing—original draft preparation, Q.T.; writing—review and editing, G.R.; visualization, T.W.; supervision, B.L.; project administration, B.L.; funding acquisition, B.L. All authors have read and agreed to the published version of the manuscript.

**Funding:** This research was funded by the China Postdoctoral Science Foundation (2022M710934), Postdoctoral Applied Research Project of Qingdao City, Project of Shandong Province Higher Educational Young Innovative Talent Introduction and Cultivation Team (Intelligent Transportation Team of Offshore Products).

**Institutional Review Board Statement:** Not applicable.

**Informed Consent Statement:** Not applicable.

**Data Availability Statement:** Not applicable.

**Conflicts of Interest:** The authors declare no conflict of interest.
