Skip to main content
SpringerLink
Log in

Multi-layer Feed-forward Neural Network Deep Learning Control with Hybrid Position and Virtual-force Algorithm for Mobile Robot Obstacle Avoidance

  • Regular Papers
  • Robot and Applications
  • Published:
International Journal of Control, Automation and Systems Aims and scope Submit manuscript

Abstract

This paper addresses the trajectory tracking and obstacle avoidance control problems for a class of mobile robot systems. Two classes of controllers are designed for the mobile robot system in the free motion, respectively. A new hybrid position virtual-force controller is designed to adjust the distance between the mobile robot and the obstacles. Since the uncertainties between the mobile robot dynamics model and obstacles degrade the performance of the obstacle avoidance system, a multi-layer feed-forward neural networks (NNs) deep learning method with hybrid position and virtual-force is proposed, such that the distance between the mobile robot and the obstacles converges to an adjustable bounded region. It is shown that the proposed controller in this paper is smooth, effective, and only uses the system output. The control design conditions are relaxed because of the developed multi-layer feed-forward NNs deep learning compensator. The simulation results and obstacle avoidance cases are performed to show the effectiveness of the proposed method.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Similar content being viewed by others

References

  1. I. W. Park, J. O. Kim, M. H. Oh, and W. S. Yang, “Realization of stabilization using feed-forward and feedback controller composition method for a mobile robot,” International Journal of Control, Automation and Systems vol. 13, no.5, pp. 1201–1211, July, 2015.

    Google Scholar 

  2. H. G. Kim and B. K. Kim, “Minimum-energy cornering trajectory planning with self-rotation for three-wheeled omni-directional mobile robots,” International Journal of Control, Automation and Systems, vol. 15, no.4, pp. 1857–1866, July 2017.

    Google Scholar 

  3. X. Liang, H. Wang, Y. H. Liu, W. Chen, and T. Liu, “Formation control of nonholonomic mobile robots without position and velocity measurements,” IEEE Transactions on Robotics, vol. 34, no. 2, pp. 434–446, April 2018.

    Article  Google Scholar 

  4. K. Alipour and A. Abbaspour, “The effect of remote center compliance parameters on formation control of cooperative wheeled mobile robots for object manipulation,” International Journal of Control, Automation and Systems, vol. 16, no. 1, pp. 306–317, 2018.

    Article  Google Scholar 

  5. L. N. Tan, “Omnidirectional vision-based distributed optimal tracking control for mobile multi-robot systems with kinematic and dynamic disturbance rejection,” IEEE Transactions on Industrial Electronics, vol. 65, no. 7, pp. 5693–5703, July 2018.

    Article  Google Scholar 

  6. H. J. Yang, X. Z. Fan, S. Peng, and C. C. Hua, “Nonlinear control for tracking and obstacle avoidance of a wheeled mobile robot with nonholonomic constraint,” IEEE Transactions on Control Systems Technology, vol. 24, no. 2, pp. 741–746, March 2016.

    Google Scholar 

  7. C. J. Kim and D. Chwa, “Obstacle avoidance method for wheeled mobile robots using interval type-2 fuzzy neural network,” IEEE Transactions on Fuzzy Systems, vol. 23, no. 3, pp. 677–687, June 2015.

    Article  Google Scholar 

  8. C. C. Cheah, S. Kawamura, and S. Arimoto, “Stability of hybrid position and force control for robotic manipulator with kinematics and dynamics uncertainties,” Automatica, vol. 39, no. 5, pp. 847–855, May 2013.

    Article  MathSciNet  MATH  Google Scholar 

  9. S. Chiaverini, B. Siciliano, and L. Villani, “Force/position regulation of compliant robot manipulators.” IEEE Transactions on Automatic Control, vol. 39, no. 3, pp. 647–652, March 2004.

    Article  MATH  Google Scholar 

  10. M. S. Ju, C. C. K. Lin, D. H. Lin, and I. S. Hwan, “A rehabilitation robot with force-position hybrid fuzzy controller: hybrid fuzzy control of rehabilitation robot,” IEEE Transactions on Neural Systems & Rehabilitation Engineering, vol. 13, no. 3, pp. 349–358, September 2005.

    Article  Google Scholar 

  11. K. Kiguchi and T. Fukuda, “Intelligent position/force controller for industrial robot manipulators-application of fuzzy neural networks,” IEEE Transactions on Industrial Electronics, vol. 44, no. 6, pp. 753–761, June, 2012.

    Article  Google Scholar 

  12. M. C. Hwang and X. Hu, “A robust position/force learning controller of manipulators via nonlinear H¥ control and neural networks,” IEEE Transactions on Systems, Man and Cybernetics, Part B, vol. 30, no. 2, pp. 310–321, April 2000.

    Article  Google Scholar 

  13. J. W. Kwon and D. Chwa, “Hierarchical formation control based on a vector field method for wheeled mobile robots,” IEEE Transactions on Robotics, vol. 28, no. 6, pp. 1335–1345, May 2012.

    Article  Google Scholar 

  14. Y. D. Kwon and J. S. Lee, “An obstacle avoidance algorithm for mobile robot: the improved weighted safety vector field method,” IEEE International Symposium on Intelligent Control, vol. 25, no. 5, pp. 441–446, August 2005.

    Google Scholar 

  15. D. R. Nelson and D. B. Barber, “Vector field path following for miniature air vehicles,” International Journal of Control, Automation and Systems, vol. 23, no. 3, pp. 519–529, June 2017.

    Google Scholar 

  16. W. H. Zhu, Z. Bien, and J. De Schutter, “Adaptive motion/ force control of multiple manipulators with joint flexibility based on virtual decomposition,” IEEE Transactions on Automatic Control, vol. 43, no. 1, pp. 46–60, January 2017.

    MathSciNet  MATH  Google Scholar 

  17. M. Lashin, M. Fanni, A. M. Mohamed, and T. Miyashita, “Dynamic modeling and inverse optimal PID with feedforward control in H-¥ framework for a novel 3D pantograph manipulator,” International Journal of Control, Automation and Systems, vol. 16, no. 1, pp. 39–54, March 2018.

    Article  Google Scholar 

  18. M. Farooq and D. B. Wang, “Hybrid force/position control scheme for flexible joint robot with friction between and the end-effector and the environment,” International Journal of Engineering Science, vol. 46, no. 12, pp. 1266–1278, December 2016.

    MathSciNet  MATH  Google Scholar 

  19. A. S. Poznyak and L. Ljung, “On-line identification and adaptive trajectory tracking for nonlinear stochastic continuous time systems using differential neural networks,” Automatica, vol. 37, no. 8, pp. 1257–1268, June 2008.

    Article  MathSciNet  MATH  Google Scholar 

  20. X. P. Cheng and R. V. Patel, “Neural network based tracking control of a flexible macro-micro manipulator system,” Neural Networks, vol. 16, no. 2, pp. 271–286, March 2013.

    Article  Google Scholar 

  21. J. Moreno-Valenzuela, C. Aguilar-Avelar, S. A. Puga-Guzmán, and V. Santibáñez, “Adaptive neural network control for the trajectory tracking of the Furuta pendulum,” IEEE Transactions on Cybernetics, vol. 46, no. 12, pp. 3439–3452, December 2017.

    Article  Google Scholar 

  22. F. Hashemzadeh and M. Tavakoli, “Position and force tracking in nonlinear teleoperation systems under varying delays,” Robotica, vol. 33, no. 4, pp. 1003–1016, June 2010.

    Article  Google Scholar 

  23. A. Kazemy and J. Cao, “Consecutive synchronization of a delayed complex dynamical network via distributed adaptive control approach,” International Journal of Control, Automation and Systems vol. 16, no. 6, pp. 2656–2664, December 2018.

    Article  Google Scholar 

  24. T. S. Li, S. C. Tong, and G. Feng, “A novel robust adaptivefuzzy-tracking control for a class of nonlinearmultiinput/multi-output systems,” IEEE Transactions on Fuzzy Systems, vol. 18, no. 1, pp. 150–160, February 2010.

    Article  Google Scholar 

  25. C. C. Hua, Y. Y. Yang, and X. P. Guan, “Neural networkbased adaptive position tracking control for bilateral teleoperation under constant time delay,” Neurocomputing, vol. 113, no. 7, pp. 204–212, August 2017.

    Google Scholar 

  26. Y. Bengio, A. Courville, and P. Vincent, “Representation Learning: A Review and New Perspectives,” IEEE Transactions on Pattern Analysis & Machine Intelligence, vol. 35, no. 8, pp. 1798–1828, August 2016.

    Article  Google Scholar 

  27. M. Längkvist. L Karlsson, and A. Loutfi, “A review of unsupervised feature learning and deep learning for timeseries modeling,” Pattern Recognition Letters, vol. 42, no. 1, pp. 11–24, June 2017.

    Google Scholar 

  28. D. Erhan, Y. Bengio, A. Courville, P.A. Manzagol, and P. Vincent, “Why does unsupervised pretraining help deep learning,” Journal of Machine Learning Research, vol. 11, no. 3, pp. 625–660, June 2017.

    MATH  Google Scholar 

  29. Y. N. Wang, T. L. Mai, and J. X. Mao, “Adaptive motion/ force control strategy for non-holonomic mobile manipulator robot using recurrent fuzzy wavelet neural networks,” Engineering Applications of Artificial Intelligence, vol. 34, pp. 137–153, September 2014.

    Article  Google Scholar 

  30. J. P. Jun, X. M. Jin, A. Pott, S. Park, J. O. Park, and S. Y. Ko, “Hybrid position/force control using an admittance control scheme in Cartesian space for a 3-DOF planar cable-driven parallel robot,” International Journal of Control, Automation and Systems, vol. 14, no. 4, pp. 1106–1113, August 2016.

    Article  Google Scholar 

  31. J. Schmidhuber, “Deep learning in neural networks: An overview,” Neural Networks, vol. 61, pp. 85–117, January 2015.

    Article  Google Scholar 

  32. Y. Y. Wang, H. Shen, H. R. Karimi, and D. P. Duan, “Dissipativity-based fuzzy integral sliding mode control of continuous-time T-S fuzzy systems,” IEEE Transactions on Fuzzy Systems, vol. 26, no. 3, pp. 1164–1176, June 2018.

    Article  Google Scholar 

  33. Y. Y. Wang, Y. B. Gao, H. R. Karimi, H. Shen, and Z. J. Fang, “Sliding mode control of fuzzy singularly perturbed systems with application to electric circuit,” IEEE Transactions on Systems Man & Cybernetics Systems, vol. 48, no. 10, pp. 1667–1675, August 2017.

    Article  Google Scholar 

  34. Y. Y. Wang, Y. Q. Xia, H. Shen, and P. F. Zhou, “SMC design for robust stabilization of nonlinear Markovian jump singular systems,” IEEE Transactions on Automatic Control, vol. 63, no. 1, pp. 219–224, June 2018.

    Article  MathSciNet  MATH  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. School of Electric Engineering, Yanshan University, No. 438, Haigang District, Qinhuangdao, P. R. China

    Wei Zheng, Hong-Bin Wang, Zhi-Ming Zhang, Ning Li & Peng-Heng Yin

Authors
  1. Wei Zheng
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Hong-Bin Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Zhi-Ming Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Ning Li
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Peng-Heng Yin
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Wei Zheng.

Additional information

Recommended by Associate Editor Jun Cheng under the direction of Editor Doo Yong Lee. The authors are grateful to the editor and the anonymous reviewers for their valuable comments and suggestions that have helped improve the presentation of the paper. This research was financially supported by National Natural Science Foundation of China [Grant No. 61473248, 61773333]; Natural Science Foundation of Hebei Province [Grant No. F2016203496, F2018203413].

Wei Zheng received the M.S. degree in Institute of Electrical Engineering from Yanshan University in 2011. Currently, she is now studying a Ph.D. degree of the Control Theory and Control Engineering in Yanshan University. Her current research interests include robust nonlinear control systems, control systems design over network, and intelligent control.

Hong-Bin Wang received the B.S. and M.S. degrees in electrical engineering and automation from Northeast National Heavy Machinery University in 1988 and 1993, respectively. Moreover, he received the Ph.D. degree in Institute of Electrical Engineering from Yanshan University in 2005. He is currently a professor in the Institute of Electrical Engineering of Yanshan University. He is the associate dean for the institute of Electrical Engineering, in 2008, and the dean of Liren institute, Yanshan University, Qinhuangdao, China, in 2016. He is the (co)author of many books, and about 60 papers, get about 10 national invention patents. As (a)an (co)-investigator, he was finished and carrying out more than 31 projects, supported by Key Construction Program of the National “863” Project; National Natural Science Foundation of China (NSFC); Natural Science Foundation of Hebei Province of China, and other important engineering project foundations where applied practically.

Zhi-Ming Zhang received the M.S. degree in Institute of Electrical Engineering from Yanshan University in 2011. Currently, he is now studying a Ph.D. degree of Control Theory and Control Engineering in Yanshan University. His research interests include robust nonlinear control and fuzzy control.

Ning Li received the B.S. degree in Jilin Institute of Chemical Technology in 2017. Currently, he is now studying an M.S. degree of Control Theory and Control Engineering in Institute of Electrical Engineering from Yanshan University. His research interests include adaptive and multi-agent system control.

Peng-Heng Yin received the B.S. degree in Department of Electrical Engineering from Liren college, Yanshan University in 2017. Currently, he is now studying an M.S. degree of Control Theory and Control Engineering in Institute of Electrical Engineering from Yanshan University. His research interests include adaptive and multi-agent system control.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Zheng, W., Wang, HB., Zhang, ZM. et al. Multi-layer Feed-forward Neural Network Deep Learning Control with Hybrid Position and Virtual-force Algorithm for Mobile Robot Obstacle Avoidance. Int. J. Control Autom. Syst. 17, 1007–1018 (2019). https://doi.org/10.1007/s12555-018-0140-8

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s12555-018-0140-8

Keywords

Search

Navigation