Abstract
This paper presents a stable ARX-Laguerre fuzzy proportional-integral-derivative observation (FPIDO) system for fault detection and identification (FDI) of actuator and sensor faults in a multi-degrees of freedom robot manipulator. An ARX-Laguerre technique is used in this paper to improve the system modeling in the presence of uncertainty and disturbance in a robot manipulator. The proposed FPIDO is applied to the ARX-Laguerre procedure to modify fault detection, estimation and identification to reduce the system’s order. Fuzzy coefficient scheduling is utilized to modify the convergence with respect to the minimum error.
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References
Siciliano, B., Khatib, O.: Springer Handbook of Robotics. Springer (2016)
Ngoc Son, N., Anh, H.P.H., Thanh Nam, N.: Robot manipulator identification based on adaptive multiple-input and multiple-output neural model optimized by advanced differential evolution algorithm. Int. J. Adv. Robot. Syst. 14, 1729881416677695 (2016)
del Titolo, T.P.I.C., di Dottore, D.R.: Model-Based Fault Diagnosis in Dynamic Systems Using Identification Techniques (2002)
Lafont, F., Balmat, J.-F., Pessel, N., Fliess, M.: A model-free control strategy for an experimental greenhouse with an application to fault accommodation. Comput. Electron. Agric. 110, 139–149 (2015)
Badihi, H., Zhang, Y., Hong, H.: Fault-tolerant cooperative control in an offshore wind farm using model-free and model-based fault detection and diagnosis approaches. Appl. Energy (2017)
Wang, X., Li, X., Wang, J., Fang, X., Zhu, X.: Data-driven model-free adaptive sliding mode control for the multi degree-of-freedom robotic exoskeleton. Inf. Sci. 327, 246–257 (2016)
Khalastchi, E., Kalech, M., Rokach, L.: A hybrid approach for improving unsupervised fault detection for robotic systems. Expert Syst. Appl. 81, 372–383 (2017)
Stavrou, D., Eliades, D.G., Panayiotou, C.G., Polycarpou, M.M.: Fault detection for service mobile robots using model-based method. Auton. Robot. 40, 383–394 (2016)
López-Estrada, F.R., Ponsart, J.-C., Theilliol, D., Zhang, Y., Astorga-Zaragoza, C.-M.: LPV model-based tracking control and robust sensor fault diagnosis for a quadrotor UAV. J. Intell. Rob. Syst. 84, 163–177 (2016)
Van, M., Franciosa, P., Ceglarek, D.: Fault diagnosis and fault-tolerant control of uncertain robot manipulators using high-order sliding mode. Math. Probl. Eng. (2016)
Anh, H.P.H., Nam, N.T.: Novel adaptive forward neural MIMO NARX model for the identification of industrial 3-DOF robot arm kinematics. Int. J. Adv. Rob. Syst. 9, 104 (2012)
Alavandar, S., Nigam, M.: Neuro-fuzzy based approach for inverse kinematics solution of industrial robot manipulators. Int. J. Comput. Commun. Control 3, 224–234 (2008)
Jami‘in, M.A., Hu, J., Marhaban, M.H., Sutrisno, I., Mariun, N.B.: Quasi‐ARX neural network based adaptive predictive control for nonlinear systems. IEEJ Trans. Electr. Electron. Eng. 11, 83–90 (2016)
Hartmann, A., Lemos, J.M., Costa, R.S., Xavier, J., Vinga, S.: Identification of switched ARX models via convex optimization and expectation maximization. J. Process Control 28, 9–16 (2015)
Bouzrara, K., Garna, T., Ragot, J., Messaoud, H.: Online identification of the ARX model expansion on Laguerre orthonormal bases with filters on model input and output. Int. J. Control 86, 369–385 (2013)
Bouzrara, K., Garna, T., Ragot, J., Messaoud, H.: Decomposition of an ARX model on Laguerre orthonormal bases. ISA Trans. 51, 848–860 (2012)
Busawon, K.K., Kabore, P.: Disturbance attenuation using proportional integral observers. Int. J. Control 74, 618–627 (2001)
Acknowledgements
This work was supported by the Korea Institute of Energy Technology Evaluation and Planning (KETEP) and the Ministry of Trade, Industry and Energy (MOTIE) of the Republic of Korea (No. 20162220100050, No. 20161120100350, and No. 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., Sohaib, M., Kim, JM. (2019). Fault Diagnosis of a Robot Manipulator Based on an ARX-Laguerre Fuzzy PID Observer. In: Kim, JH., et al. Robot Intelligence Technology and Applications 5. RiTA 2017. Advances in Intelligent Systems and Computing, vol 751. Springer, Cham. https://doi.org/10.1007/978-3-319-78452-6_33
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DOI: https://doi.org/10.1007/978-3-319-78452-6_33
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