Designing an effective procedure for fault detection and identification (FDI) is necessary to maintain the healthy and safe operation of robot manipulators. The complexities of nonlinear parameters inherent in a robot manipulator make it challenging to detect and identify faults. To address this issue, a powerful, robust, hybrid fault identification method based on the fuzzy extended ARX-Laguerre proportional integral (PI) observer for perturbation robot manipulators is presented. Accurate fault estimation is an essential challenge in classical extended ARX-Laguerre PI observers. The Takagi-Sugeno (T-S) fuzzy algorithm is applied to the sliding mode extended ARX-Laguerre PI observer to modify the performance of fault estimation. Moreover, using the ARX-Laguerre algorithm, PI observation technique, sliding mode estimation method, and T-S fuzzy procedure, the system’s performance showed fast convergence and high accuracy. A PUMA robot manipulator was used to test the effectiveness of the proposed method. Results indicated that the proposed algorithm outperforms the ARX-Laguerre PI observer performance.
CITATION STYLE
Piltan, F., & Kim, J. M. (2020). Advanced fuzzy observer-based fault identification for robot manipulators. In Advances in Intelligent Systems and Computing (Vol. 1029, pp. 141–148). Springer Verlag. https://doi.org/10.1007/978-3-030-23756-1_19
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