Abstract
This paper proposes a model-based fault detection and diagnosis (FDD) technique for six degrees of freedom PUMA robot manipulator in presence of noise in actuator and sensor faults. The inverse modeling based on an adaptive method, which combines the fuzzy C-means clustering with the modified autoregressive eXternal (ARX) model, is presented for the system identification. The proposed adaptive nonlinear ARX fuzzy C-means (NARXNF) clustering technique obtains an improved convergence and error reduction than that of the traditional fuzzy C-means clustering algorithm. In addition, proportional integral (PI) feedback linearization observation is used for diagnosing the fault, where the convergence, robustness, and stability are validated by fuzzy linear matrix inequality (FLMI). Experimental results, in presence of 40% noise, show that the rate of root mean square (RMS) error for end-effector position is less than 0.00624. The proposed method also improves the rate of sensors and actuators FDD without additional hardware.
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Acknowledgements
This work was supported by the Korea Institute of Energy Technology Evaluation and Planning (KETEP) and the Ministry of Trade, Industry & Energy (MOTIE) of the Republic of Korea (Nos. 20162220100050, 20161120100350, 20172510102130). It was also funded in part by The Leading Human Resource Training Program of Regional Neo industry through the National Research Foundation of Korea (NRF) funded by the Ministry of Science, ICT and future Planning (NRF-2016H1D5A1910564), and in part by the Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education (2016R1D1A3B03931927).
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Piltan, F., Islam, M., Kim, JM. (2019). Input-Output Fault Diagnosis in Robot Manipulator Using Fuzzy LMI-Tuned PI Feedback Linearization Observer Based on Nonlinear Intelligent ARX Model. In: Bhatia, S., Tiwari, S., Mishra, K., Trivedi, M. (eds) Advances in Computer Communication and Computational Sciences. Advances in Intelligent Systems and Computing, vol 759. Springer, Singapore. https://doi.org/10.1007/978-981-13-0341-8_28
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DOI: https://doi.org/10.1007/978-981-13-0341-8_28
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