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.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Siciliano, B., Khatib, O. (eds): 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(1), 1729881416677695 (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. Robot. Syst. 9(4), 104 (2012)
Weyer, Erik: Finite sample properties of system identification of ARX models under mixing conditions. Automatica 36(9), 1291–1299 (2000)
Zhao, Wen-Xiao, Chen, Han-Fu: Recursive identification for Hammerstein system with ARX subsystem. IEEE Trans. Autom. Control 51(12), 1966–1974 (2006)
Al-Dabbagh, R.D., Kinsheel, A., Mekhilef, S., Baba, M.S., Shamshirband, S.: System identification and control of robot manipulator based on fuzzy adaptive differential evolution algorithm. Adv. Eng. Softw. 78, 60–66 (2014)
Alavandar, Srinivasan, Nigam, M.J.: Neuro-fuzzy based approach for inverse kinematics solution of industrial robot manipulators. Int. J. Comput. Commun. Control 3(3), 224–234 (2008)
Wu, L., et al.: Fault detection for underactuated manipulators modeled by Markovian jump systems. IEEE Trans. Ind. Electron. 63(7), 4387–4399 (2016)
Aleksovski, D., et al.: A comparison of fuzzy identification methods on benchmark datasets. IFAC-PapersOnLine 49(5), 31–36 (2016)
Jami‘in, M.A., et al.: Quasi‐ARX neural network based adaptive predictive control for nonlinear systems. IEEE Trans. Electr. Electron. Eng. 11(1), 83–90 (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 (2016)
Sarıoğlu, A., Kural, A.: Modeling and ARX identification of a quadrotor MiniUAV. In: 2015 9th International Conference on Electrical and Electronics Engineering (ELECO. IEEE (2015)
Hartmann, A., et al.: Identification of switched ARX models via convex optimization and expectation maximization. J. Process Control 28, 9–16 (2015)
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).
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2019 Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
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
Download citation
DOI: https://doi.org/10.1007/978-981-13-0341-8_28
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-13-0340-1
Online ISBN: 978-981-13-0341-8
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)