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Robot Intelligence Technology and Applications 2 pp 433–447Cite as

Trajectory Tracking Control Using Echo State Networks for the CoroBot’s Arm

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Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 274))

Abstract

Different neural network models have proven being useful for tracking purposes in robotic devices. However, some models have shown superior performances to others that generate a large computational cost. This is the case of recurrent neural networks, which due to the temporal relationship existing allows satisfactory answers. Furthermore, training used by traditional algorithms, require a relatively high convergence time for some applications, especially those that are on-line. Given this problematic, this paper suggests use Echo State Networks (ESN) to perform such tasks. Additionally, results are presented for two sets of predefined tests, which were used to validate control behavior of trajectories in a manipulator embedded in a mobile platform. The results presented are related to the planar control of the manipulator in a closed loop.

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References

  1. Hu, L., Zhao, P., Lu, Z.: Trajectory tracking control based on global fast terminal sliding mode for 2-DOF manipulator. In: 2nd International conference on Intelligent Control and Information Processing (ICICIP), vol. 1, pp. 65–69 (2011)

    Google Scholar 

  2. Botsali, F.M., Kalyoncu, M., Tinkir, M., Onen, U.: Fuzzy logic trajectory control of flexible robot manipulator with rotating prismatic joint. In: 2nd International Conference on Computer and Automation Engineering (ICCAE), vol. 3, pp. 35–39 (2010)

    Google Scholar 

  3. Jurado, F., Flores, M.A., Castaneda, C.E.: Continuos-time neural control for a 2 DOF vertical robot manipulator. In: 8th International Conference on Electrical Engineering Computing Science and Automatic Control (CCE), pp. 1–6 (2011)

    Google Scholar 

  4. Stulp, F., Theodoruo, E.A., Schaal, S.: Reinforcement Learning of clothing assistance with a dual-arm robot. IEEE Transactions on Robotics 28, 1360–1370 (2012)

    Article  Google Scholar 

  5. Lukoševičius, M.: A practical guide to applying echo state networks. In: Montavon, G., Orr, G.B., Müller, K.-R. (eds.) Neural Networks: Tricks of the Trade, 2nd edn. LNCS, vol. 7700, pp. 659–686. Springer, Heidelberg (2012)

    Google Scholar 

  6. Jaeger, H.: Reservoir riddles: suggestions for echo state network research (extended abstract). In: IEEE International Joint Conference on Neural Networks, vol. 3, pp. 1460–1462 (2005)

    Google Scholar 

  7. Lukoševièius, M., Jaeger, H.: Reservoir computing approaches to recurrent neural network training. Computer Science Review 3(3), 127–149 (2009)

    Article  Google Scholar 

  8. Ganesh, K., Shishir, B.: Effects of spectral radius and settling time in the performance of echo state networks. Neural Networks 22(7), 861–863 (2009)

    Article  Google Scholar 

  9. Valencia, C.H., Vellasco, M.M.B.R., Figueiredo, K.T.: Comparative Analysis of Arm Control Performance Using Computational Intelligence. In: Kim, J.-H., Matson, E., Myung, H., Xu, P. (eds.) Robot Intelligence Technology and Applications. AISC, vol. 208, pp. 187–197. Springer, Heidelberg (2013)

    Google Scholar 

  10. Liu, D., Chen, H., Jiang, R., Liu, W.: Study of ride comfort of active suspension based on model reference neural network control system. In: Sixth International Conference on Natural Computation (ICNC), vol. 4, pp. 1860–1864 (2010)

    Google Scholar 

  11. Hoang, T.T., Hiep, D.T., Duong, B.G., Vinh, T.Q.: Trajectory tracking control of the nonholonomic mobile robot using torque method an neural network. In: 8th IEEE Conference on Industrial Electronics and Applications (ICIEA), pp. 1898–1803 (2013)

    Google Scholar 

  12. Olaru, A.: The optimizing space trajectory by using the inverse kinematics, direct dynamics and intelligent damper controlling with proper neural networks. In: International Conference on Advanced Mechatronic Systems (ICAMechS), pp. 504–509 (2012)

    Google Scholar 

  13. Chih-Lyang, H.: Hybrid neural network under-actuated sliding-mode control for trajectory tracking of quad-rotor unmanned aerial vehicle. In: International Joint Conference on Neural Networks (IJCNN), pp. 1–8 (2012)

    Google Scholar 

  14. Wu, J., Jian, H., Yongji, W., Kexin, X.: RLS-ESN based PID control for rehabilitation robotic arms driven by PM-TS actuators. In: International Conference on Modelling, Identification and Control (ICMIC), pp. 511–516 (2010)

    Google Scholar 

  15. Ni, S., Lei, L., Zhishen, W., Wang, Z.: Predictive Control of Vehicle Based on Echo State Network. In: International Conference on Intelligence Science and Information Engineering (ISIE), pp. 37–40 (2011)

    Google Scholar 

  16. Hartland, C., Bredeche, N.: Using echo state networks for robot navigation behavior acquisition. In: IEEE International Conference on Robotics and Biometrics (ROBIO), pp. 201–206 (2007)

    Google Scholar 

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Author information

Authors and Affiliations

  1. LIRA – Computational Intelligence and Applied Robotics Laboratory, Electric Engineering Department, Pontifical Catholic University of Rio de Janeiro (PUC-Rio), R. Marquês S. Vicente 225 – Gávea, Rio de Janeiro, RJ, Brasil

    Cesar H. Valencia, Marley M. B. R. Vellasco & Karla T. Figueiredo

Authors
  1. Cesar H. Valencia
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  2. Marley M. B. R. Vellasco
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  3. Karla T. Figueiredo
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Corresponding author

Correspondence to Cesar H. Valencia .

Editor information

Editors and Affiliations

  1. Electrical Engineering, KAIST, Daejeon, Korea, Republic of (South Korea)

    Jong-Hwan Kim

  2. Computer and Information Technology, Purdue University, West Lafayette, Indiana, USA

    Eric T . Matson

  3. Civil and Environmental Engg., KAIST, Daejeon, Korea, Republic of (South Korea)

    Hyun Myung

  4. Department of Mechanical Engineering, Auckland University, Auckland, New Zealand

    Peter Xu

  5. University of Waterloo, Waterloo, Ontario, Canada

    Fakhri Karray

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© 2014 Springer International Publishing Switzerland

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Valencia, C.H., Vellasco, M.M.B.R., Figueiredo, K.T. (2014). Trajectory Tracking Control Using Echo State Networks for the CoroBot’s Arm. In: Kim, JH., Matson, E., Myung, H., Xu, P., Karray, F. (eds) Robot Intelligence Technology and Applications 2. Advances in Intelligent Systems and Computing, vol 274. Springer, Cham. https://doi.org/10.1007/978-3-319-05582-4_38

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  • DOI: https://doi.org/10.1007/978-3-319-05582-4_38

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-05581-7

  • Online ISBN: 978-3-319-05582-4

  • eBook Packages: EngineeringEngineering (R0)

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