Abstract
In this paper, a multi-layered feed-forward neural network is trained on-line by robust adaptive dead zone scheme to identify simulated faults occurring in the robot system and reconfigure the control law to prevent the tracking performance from deteriorating in the presence of system uncertainty. Consider the fact that system uncertainty can not be known a priori, the proposed robust adaptive dead zone scheme can estimate the upper bound of system uncertainty on line to ensure convergence of the training algorithm, in turn the stability of the control system. A discrete-time robust weight-tuning algorithm using the adaptive dead zone scheme is presented with a complete convergence proof. The effectiveness of the proposed methodology has been shown by simulations for a two-link robot manipulator.
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Blanke, M. et al.: Fault-tolerant control systems - a history view, Control Engineering Practice 5(5) (1997), 693–702.
Li, Z. and Zheng, Y.: Fault diagnosis of dynamic system by means of parameter estimation, Proc. of IFAC Iden. and System Parameter Estimation, Beijing, 1988, pp. 1159–1164.
Frank, P. M.: Fault diagnosis in dynamic systems using analytical and knowledge-based redundancy, Automatica 26(3) (1990), 459–474.
Vemuri, A. T., Polycarpou, M. M., and Diakourtis, S. A.: Neural network based fault detection in robotic manipulators, IEEE Trans. Robotics Automat. 14(2) (1998), 12–24.
Vemuri, T. and Polycarpou, M. M.: Neural-network-based robust fault diagnosis in robot systems, IEEE Trans. Neural Networks 8(6) (1997), 1410–1420.
Heiss, M.: Error-minimizing dead zone for basis function networks, IEEE Trans. Neural Networks 7(6) (1996), 39–50.
Song, Q., Xiao, J., and Soh, Y. C.: Robust back-propagation training algorithm for multi-layered neural tracking controller, IEEE Trans. Neural Networks 10(5) (1999), 1133–1141.
Samson, C.: Stability analysis of adaptively controlled system subject to bounded disturbances, Automatica 19 (1983), 81–86.
Ortega, R., Praly, L., and Landau, I. D.: Robustness of discrete-time direct adaptive controllers, IEEE Trans. Automatic Control 30(5) (1985), 1179–1187.
Narendra, K. S. and Annaswamy, A. M.: Stable Adaptive Systems, Prentice-Hall, Englewood Cliffs, NJ, 1989.
Jagannathan, S. and Lewis, F. L.: Multi-layer discrete-time neural-net controller with guaranteed performance, IEEE Trans. Neural Networks 7(1) (1996), 107–130.
Chen, F. C. and Khalil, H. K.: Adaptive control of a class of nonlinear discrete-time systems using neural networks, IEEE Trans. Automatic Control 40(5) (1995), 791–801.
Polycarpou, M. M. and Ioannou, P. A.: Learning and convergence analysis of neural-type structured networks, IEEE Trans. Neural Networks 3(1) (1992), 39–50.
Ishihara, T.: Direct digital design of computed torque controllers, J. Robotic Systems 11(3) (1994), 197–209.
Lewis, F. L., Abdallah, C. T., and Dawson, D. M.: Control of Robot Manipulators, McMillan, New York, 1993.
Goodwin, G. and Sin, K. S.: Adaptive Filtering, Prediction and Control, Prentice-Hall, Englewood Cliffs, NJ, 1984.
Kosko, B.: Neural Networks and Fuzzy Systems, Prentice-Hall, Englewood Cliffs, NJ, 1992.
Ljung, L. and Soderstrom, T.: Theory and Practice of Recursive Identification, MIT Press, Cambridge, MA, 1983.
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Song, Q., Hu, W.J., Yin, L. et al. Robust Adaptive Dead Zone Technology for Fault-Tolerant Control of Robot Manipulators Using Neural Networks. Journal of Intelligent and Robotic Systems 33, 113–137 (2002). https://doi.org/10.1023/A:1014603028024
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DOI: https://doi.org/10.1023/A:1014603028024