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


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

November 26, 2012
May 16, 2013
July 20, 2013
recurrent neural network, elastic drive system, friction, neural network observer, control
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:
A. Jafari, R. Dhaouadi, and A. 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:
  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.

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