single-jc.php

JACIII Vol.17 No.4 pp. 637-646
doi: 10.20965/jaciii.2013.p0637
(2013)

Paper:

Nonlinear Friction Estimation in Elastic Drive Systems Using a Dynamic Neural Network-Based Observer

Amir Hossein Jafari*, Rached Dhaouadi**, and Ali Jhemi**

*School of Electrical and Computer Engineering, Oklahoma State University, Stillwater, OK, USA

**College of Engineering, American University of Sharjah, Sharjah, United Arab Emirates

Received:
November 26, 2012
Accepted:
May 16, 2013
Published:
July 20, 2013
Keywords:
recurrent neural network, elastic drive system, friction, neural network observer, control
Abstract

This paper presents a neural-network based observer for nonlinear elastic drive systems. The proposed nonlinear observer uses a Diagonal Recurrent Neural Network (DRNN) combined with the dynamics of a linear Two-Mass-Model (2MM) system to identify nonlinear characteristics of the drive system such as Coulomb and nonlinear viscous friction torques. Theoretical analysis of the proposed neural-network based observer, including the neural network structure and the training algorithm convergence, are presented and discussed. Simulation results are confirmed experimentally using a 2MM system setup.

Cite this article as:
Amir Hossein Jafari, Rached Dhaouadi, and Ali Jhemi, “Nonlinear Friction Estimation in Elastic Drive Systems Using a Dynamic Neural Network-Based Observer,” J. Adv. Comput. Intell. Intell. Inform., Vol.17, No.4, pp. 637-646, 2013.
Data files:
References
  1. [1] S. Beineke, H. Wertz, F. Schutte, H. Grotstollen, and N. Frohleke, “Identification of nonlinear two-mass systems for self-commissioning speed control of electrical drives,” In Proc. of the 24th Annual Conf. of the IEEE Industrial Electronics Society (IECON ’98), Vol.4, pp. 2251-2256, 1998.
  2. [2] B. Lantos, “Some Applications of Soft Computing Methods in System Modeling and Control,” J. of Advanced Computational Intelligence and Intelligent Informatics, Vol.2, No.3, pp. 82-87, 1998.
  3. [3] K. Szabat, T. Orlowska-Kowalska, and P. Serkies, “Robust Control of the Two-mass Drive System Using Model Predictive Control,” Chapter 22, pp. 489-507, Wroclaw University of Technology, April 2011.
  4. [4] R. Dhaouadi, “Torque Control in Harmonic Drives with Nonlinear Dynamic Friction Compensation,” J. of Robotics and Mechatronics, Vol.16, No.4, pp. 388-396, 2004.
  5. [5] S. N.Vukosavic and M. R. Stoji, “Suppression of Torsional Oscillations in a High-Performance Speed Servo Drive,” IEEE Trans. on Industrial Electronics, Vol.45, pp. 108-117, February 1998.
  6. [6] D. G. Luenberger, “An Introduction to Observers,” IEEE Trans. on Automatic Control, Vol.16, pp. 596-602, December 1971.
  7. [7] F. Abdollahi, H. Talebi, and R. V. Patel, “A stable neural networkbased observer with application to flexible-joint manipulators,” IEEE Trans. on Neural Networks, Vol.17, pp. 118-129, January 2006.
  8. [8] B. Daâchi and A. Benallegue, “Stable Neural Network Controller Based Observer for Rigid Robot Manipulators,” J. of Robotics and Mechatronics, Vol.15, No.1, pp. 77-83, 2003.
  9. [9] R. Dhaouadi and K. Nouri, “Neural Network-Based Speed Control of A Two-Mass-Model System,” J. of Advanced Computational Intelligence, Vol.3, No.5, pp. 427-430, 1999.
  10. [10] H. Gong, H. Xu, and F. N. Chowdhury, “A Neuro-augmented Observer for a Class of Nonlinear Systems,” In Int. Joint Conf. on Neural Networks, pp. 2497-2500, Canada, July 2006.
  11. [11] K. Nouri, R. Dhaouadi, and N. B. Braiek, “A New Adaptive Control Scheme Using Dynamic Neural Networks,” J. of Robotics and Mechatronics, Vol.20, No.1, pp. 171-177, 2008.
  12. [12] R. P. Landim, F. A. S. Neves, S. R. Silva, W. M. Caminhas, and B. R. Menezes, “Online Neofuzzy Neuron Flux Observer for Induction Motor Drives,” J. of Advanced Computational Intelligence and Intelligent Informatics, Vol.6, No.2, pp. 84-92, 2002.
  13. [13] S. Seshagiri and H. K. Khalil, “Output Feedback Control of Nonlinear Systems Using RBF Neural Networks,” IEEE Trans. on Neural Networks, Vol.11, No.1, January 2000.
  14. [14] C. C. de. Wit, H. Olsson, K. J. Astrom, and P. Lischinsky, “A New Model for Control of Systems with Friction,” IEEE Trans. on AutomaticControl, Vol.40, No.5, pp. 419-425, 1995.
  15. [15] C.-C. Ku and K. Y. Lee, “Diagonal Recurrent Neural Networks for Dynamic Systems Control,” IEEE Trans. on Neural Networks, Vol.6, pp. 144-156, 1995.
  16. [16] R. Jafari and M. Hagan, “Global stability analysis using the method of Reduction Of Dissipativity Domain,” In Int. Joint Conf. on Neural Networks (IJCNN), pp. 2550-2556, 2011.
  17. [17] M. M. Polycarpou and P. A. Ioannou, “Learning and convergence analysis of neural-type structured networks,” IEEE Trans. on Neural Networks, Vol.3, No.1, pp. 39-50, January 1992.

*This site is desgined based on HTML5 and CSS3 for modern browsers, e.g. Chrome, Firefox, Safari, Edge, Opera.

Last updated on Aug. 03, 2021