Abstract
In this paper, a new fault diagnosis and fault tolerant control algorithm for manipulators with actuator multiplicative fault is proposed. The dynamic model of the manipulator with disturbance is taken as the research object. When faults occur in the actuator, a nonlinear observer based on radial basis function (RBF) neural network is used to estimate the fault information. After the fault information is obtained, an adaptive back-stepping sliding mode controller is used to control the manipulator to reach the desired trajectory. At last, an illustrated example is given to demonstrate the efficiency of the proposed algorithm, and satisfactory results have been obtained.
Similar content being viewed by others
References
Z. Gao and S. X. Ding, “Actuator fault robust estimation and fault-tolerant control for a class of nonlinear descriptor systems,” Automatica, vol. 43, no. 5, pp. 912–920, 2007.
Y. Gao, F. Xiao, J. Liu, and R. Wang, “Distributed soft fault detection for interval type-2 fuzzy-model-based stochastic systems with wireless sensor networks,” IEEE Transactions and Industrial Informatics, vol. 11, no. 10, pp. 84–89, 2017.
J. Cheng, J. H. Park, X. Zhao, H. R. Karimi, and J. Cao, “Quantized nonstationary filtering of networked Markov switching RSNSs: A multiple hierarchical structure strategy,” IEEE Transactions on Automatic Control, pp. 1–8, December 2019. DOI: https://doi.org/10.1109/TAC.2019.2958824
K. M. Ali and T. Hamed, “Adaptive fuzzy fault tolerant control using dynamic sliding mode,” International Journal of Control, Automation and Systems, vol. 16, no. 1, pp. 360–367, January 2018.
Y. Gao, J. Liu, G. Sun, M. Liu, and L. Wu, “Fault deviation estimation and integral sliding mode control design for Lipschitz nonlinear systems,” Systems and Control Letters, vol. 123, pp 8–15, 2019.
X. Yan, B. Tian, and W. Hong, “An adaptive observer-based fault detection and diagnosis for nonlinear systems with sensor and actuator faults,” Proc. of International Conference on Advanced Mechatronic Systems, pp. 491–496, 2015.
K. Mohamed, M. Chadli, and M. Chaabane, “Unknown inputs observer for a class of nonlinear uncertain systems: An LMI approach,” International Journal of Automation and Computing, vol. 9, no. 3, pp. 331–336, 2012.
A. Akhenak, M. Chadli, and D. Maquin, “Sliding mode multiple observer for fault detection and isolation,” Proc. of IEEE Conference on Decision and Control, pp. 953–958, 2003.
L. M. Capisani, A. Ferrara, A. F. de Loza, and L. M. Fridman, “Manipulator fault diagnosis via higher order slidingmode observers,” IEEE Transactions on Industrial Electronics, vol. 59, no. 10, pp. 3979–3986, 2012.
F. Caccavale, P. Cilibrizzi, and F. Pierri, “Actuators fault diagnosis for robot manipulators with uncertain model,” Control Engineering Practice, vol. 17, no. 1, pp. 146–157, 2009.
H. J. Ma and G. H. Yang, “Simultaneous fault diagnosis for robot manipulators with actuator and sensor faults,” Information Sciences, vol. 366, no. 12, pp. 12–30, 2016.
P. Gaetano, P. Francesco, and C. Fabrizio, “Robust fault detection and isolation for proprioceptive sensors of robot manipulators,” Mechatronics, vol. 20, no. 1, pp. 162–170, 2010.
S. C. Guo, M. H. Yang, and Z. R. Xing, “Actuator fault detection and isolation for robot manipulators with the adaptive observer,” Advanced Materials Research, vol. 483, pp 529–532, 2012.
Z. Skaf, H. Wang, and L. Guo, “Fault tolerant control based on stochastic distribution via RBF neural networks,” Journal of Systems Engineering and Electronics, vol. 22, no. 1, pp. 63–69, 2011.
M. Chen, C. S. Jiang, and Q. X. Wu, “Sensor fault diagnosis for a class of time delay uncertain nonlinear systems using neural network,” International Journal of Automation and Computing, vol. 5, no. 4, pp. 401–405, April 2008.
Y. Yin, J. Liu, and A. S. Juan, “Observer-based adaptive sliding mode control of NPC converters: An RBF neural network approach,” IEEE Transactions on Power Electronics, vol. 34, no. 4, pp. 3831–3841, 2018.
M. X. Jia, F. L. Wang, and D. K. He, “Robust nonlinear fault diagnosis for sensors based on the RBF neural network,” Journal of Northeastern University, vol. 25, no. 8, pp. 719–722, 2004.
Z. Shen, Y. Ma, and Y. Song, “Robust adaptive fault-tolerant control of mobile robots with varying center of mass,” IEEE Transactions on Industrial Electronics, vol. 65, no. 3, pp. 2419–2428, 2017.
M. J. Er and Y. Gao, “Robust adaptive control of robot manipulators using generalized fuzzy neural networks,” IEEE Transactions on Industrial Electronics, vol. 50, no. 3, pp. 620–628, 2003.
M. L. Mcintyre, W. E. Dixon, and D. M. Dawson, “Fault identification for robot manipulators,” IEEE Transactions on Robotics, vol. 21, no. 5, pp. 1028–1034, 2005.
K. Y. Chen, “Robust optimal adaptive sliding mode control with the disturbance observer for a manipulator robot system,” International Journal of Control, vol. 16, no. 6, pp. 1701–1705, 2018.
M. D. Tran and H. J. Kang, “Adaptive terminal sliding mode control of uncertain robotic manipulators based on local approximation of a dynamic system,” Neurocomputing, vol. 228, no. 7, pp. 231–240, March 2016.
H. Yang, Y. Yang, Y. Yuan, and X. Fan, “Back-stepping control of two-link flexible manipulator based on an extended state observer,” Advances in Space Research, vol. 56, no. 10, pp. 2312–2322, 2015.
N. Nikdel, M. A. Badamchizadeh, V. Azimirad, and M. A. Nazar, “Adaptive backstepping control for an N-degree of freedom robotic manipulator based on combined state augmentation,” Robotics and Computer Integrated Manufacturing, vol. 44, pp 129–143, 2017.
F. Caccavale and I. D. Walker, “Observer-based fault detection for robot manipulators,” Proc. of IEEE International Conference on Robotics and Automation, pp. 2881–2887, 1997.
A. Freddi, S. Longhi, A. Monteriu, D. Ortenzi, and D. P. Pagnotta, “Fault tolerant control acheme for robotic manipulators affected by torque faults,” IFAC-PapersOnLine, vol. 51, no. 24, pp. 886–893, August 2018.
M. Van, M. Mavrovouniotis, and S. S. Ge, “An adaptive backstepping nonsingular fast terminal sliding mode control for robust fault tolerant control of robot manipulators,” IEEE Transactions on Systems, Man, and Cybernetics: Systems, vol. 49, no. 7, pp. 1448–1458, July 2019.
Author information
Authors and Affiliations
Corresponding author
Additional information
Publisher’s Note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Recommended by Associate Editor Aldo Jonathan Munoz-Vazquez under the direction of Editor Jessie (Ju H.) Park. The authors would like to thank the financial support received from Chinese NSFC grant 61973278. In addition, the authors also would like to thank Professor Hong Wang for his help in modifying English.
Yawei Wu received his B.S. degree from Zhengzhou University in 2018. He is currently pursuing an M.S. degree with the Zhengzhou University. His research interests include fault diagnosis and fault tolerant control for multi-agent systems.
Lina Yao received her Ph.D. degree in control theory and control engineering from the Institute of Automation, Chinese Academy of Sciences, Beijing, China, in 2006. From September 2007 to March 2008, she was a Research Fellow in University of Science and Technology of Lille, France. She is currently a Professor in the School of Electrical Engineering, Zhengzhou University, China. Her research interests include fault diagnosis and fault tolerant control of dynamic systems, stochastic distribution control and their applications.
Rights and permissions
About this article
Cite this article
Wu, Y., Yao, L. Fault Diagnosis and Fault Tolerant Control for Manipulator with Actuator Multiplicative Fault. Int. J. Control Autom. Syst. 19, 980–987 (2021). https://doi.org/10.1007/s12555-019-1013-5
Received:
Revised:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s12555-019-1013-5