Skip to main content
SpringerLink
Log in

Intelligent and Hybrid Control Techniques for Robotic Manipulator

  • Conference paper
  • First Online:
International Conference on Intelligent Computing and Smart Communication 2019

Part of the book series: Algorithms for Intelligent Systems ((AIS))

Abstract

Robotic manipulators have several industrial applications. The robotic manipulator’s control has created a challenging field of research. Traditionally, it is controlled by using conventional control method such as PID controller. However, conventional techniques are based on complex mathematical models and are referred to as a model control system. With the advancement of technology, now engineers utilize the soft computing techniques. Soft computing techniques are preferred over model control system for robotic manipulator control which is difficult to be described with the help of a mathematical model. This paper mainly focuses on the different intelligent control techniques for robotic manipulators. A brief review on the literature is discussed and an introduction to intelligent and hybrid monitoring techniques is presented.

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

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 259.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 329.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 329.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Similar content being viewed by others

References

  1. H. Chaudhary, V. Panwar, R.P.N. Sukavanam, Adaptive neuro fuzzy based hybrid force/position control for an industrial robot manipulator (2014)

    Google Scholar 

  2. S.G. Ahmad, A.S. Elbanna, Dynamic modelling with a modified PID controller of a three link rigid dynamic modelling with a modified PID controller of a three link rigid manipulator (2018)

    Google Scholar 

  3. R. García-Rodríguez, V. Parra-Vega, Task-space neuro-sliding mode control of robot manipulators under jacobian uncertainties. Int. J. Control Autom. Syst. 9(5), 895–904 (2011)

    Article  Google Scholar 

  4. F. Piltan, A. Nabaee, M. Ebrahimi, M. Bazregar, Design robust fuzzy sliding mode control technique for robot manipulator systems with modeling uncertainties. Int. J. Inf. Technol. Comput. Sci. 5(8), 123–135 (2013)

    Google Scholar 

  5. N.K. Sinha, C. Ls, 4l7 introduction, vol. 7 (1998), pp. 356–363

    Google Scholar 

  6. S.S. Roy, D.K. Pratihar, A genetic-fuzzy approach for optimal path-planning of a robotic manipulator among static obstacles. J. Inst. Eng. (India), Part CP Comput. Eng. Div. 84(May), 15–22 (2003)

    Google Scholar 

  7. L. Wang, T. Chai, C. Yang, Neural-network-based contouring control for robotic manipulators in operational space. IEEE Trans. Control Syst. Technol. 20(4), 1073–1080 (2012)

    Google Scholar 

  8. K.A. Khoiy, F. Davatgarzadeh, M. Taheri, D. Branch, A review on fuzzy-logic method to control robotic manipulator systems. 1, 40–47 (2013)

    Google Scholar 

  9. G. Marcos, J.A.T. Machado, T. Azevedo-perdicoúlis, Trajectory planning of redundant manipulators using genetic algorithms. Commun. Nonlinear Sci. Numer. Simul. 14(7), 2858–2869 (2009)

    Article  Google Scholar 

  10. J.A. Saleh, F. Karray, Soft computing techniques in intelligent wall following control for a car-like mobile robot (2009, 2014)

    Google Scholar 

  11. Y. Li, Intelligent control with application to robotics (2000, 2014)

    Google Scholar 

  12. S. Alavandar, M.J. Nigam, Tracking control of 3-DOF robot manipulator using genetic algorithm m tuned fuzzy PID controller (2005), pp. 15–24

    Google Scholar 

  13. L. Jin, S. Li, J. Yu, J. He, Robot manipulator control using neural networks : a survey R. Neurocomputing 0, 1–12 (2018)

    Google Scholar 

  14. W. He, Z. Yan, Y. Sun, Y. Ou, C. Sun, Neural-learning-based control for a constrained (2018), pp. 1–11

    Google Scholar 

  15. S. Li, Y. Zhang, L. Jin, Kinematic control of redundant manipulators using neural networks (2016), pp. 1–12

    Google Scholar 

  16. M.I. Al-saedi, H. Wu, H. Handroos, ANFIS and fuzzy tuning of PID controller for trajectory tracking of a flexible hydraulically driven parallel robot machine. J. Autom. Control Eng. 1(2), 1–8 (2013)

    Google Scholar 

  17. H.N. Rahimi, M. Nazemizadeh, Dynamic analysis and intelligent control techniques for flexible manipulators: a review. Adv. Robot. 28(2), 63–76 (2014)

    Article  Google Scholar 

  18. Z. Bingül, O. Karahan, A fuzzy logic Controller tuned with PSO for 2 DOF robot trajectory control. Expert Syst. Appl. 38(1), 1017–1031 (2011)

    Article  Google Scholar 

  19. K. Lochan, B.K. Roy, F. Detection, in Proceedings of Fourth International Conference on Soft Computing for Problem Solving, vol. 335, Dec 2015

    Google Scholar 

  20. I.H. Akyüz, Z. Bingül, S. Kizir, Cascade fuzzy logic control of a single-link flexible-joint manipulator. Turkish J. Electr. Eng. Comput. Sci. 20(5), 713–726 (2012)

    Google Scholar 

  21. B. Kazem, A. Mahdi, A. Oudah, Motion planning for a robot arm by using genetic algorithm. JJMIE 2(3), 131–136 (2008)

    Google Scholar 

  22. L. Tian, C. Collins, An effective robot trajectory planning method using a genetic algorithm. Mechatronics 14(5), 455–470 (2004)

    Article  Google Scholar 

  23. J. Tu, S.X. Yang, Genetic algorithm based path planning for a mobile robot, in 2003 IEEE International Conference on Robotics and Automation (Cat. No.03CH37422), vol. 1, no. 0 (2003), pp. 1221–1226

    Google Scholar 

  24. S. Manjaree, V. Agarwal, B.C. Nakra, Inverse kinematics using neuro-fuzzy intelligent technique for robotic manipulator. 4, 160–165 (2013)

    Google Scholar 

  25. I. Branch, A new method for position control of a 2-DOF robot arm using neuro-fuzzy controller. 5(3), 2253–2257 (2012)

    Google Scholar 

  26. F. Terms, Neuro-fuzzy system for robotics applications. 2063 (2016)

    Google Scholar 

  27. R. Köker, A genetic algorithm approach to a neural-network-based inverse kinematics solution of robotic manipulators based on error minimization. Inf. Sci. (Ny) 222, 528–543 (2013)

    Article  MathSciNet  Google Scholar 

  28. R. Köker, Full Length research paper a neuro-genetic approach to the inverse kinematics solution of robotic manipulators. 6(13), 2784–2794 (2011)

    Google Scholar 

  29. P. Kalra, N.R. Prakash, A neuro-genetic algorithm approach for solving the inverse kinematics of robotic manipulators, in Proceedings of IEEE International Conference on Systems, Man and Cybernetics, vol. 2 (2003), pp. 1979–1984

    Google Scholar 

  30. S. Refou, K. Benmahammed, Control of a manipulator robot by neuro-fuzzy subsets form approach control optimized by the genetic algorithms, pp. 1–13 (2018)

    Google Scholar 

  31. G. Mester, Neuro-fuzzy-genetic controller design for robot manipulators. Integr. Vlsi J. 87–92 (1995)

    Google Scholar 

  32. T. Khelchandra, J. Huang, S. Debnath, Path planning of mobile robot with neuro-genetic-fuzzy technique in static environment, in International Conference on Intelligent Systems Design and Applications, April 2012, pp. 149–154

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. School of Electronics and Electrical Engineering, Lovely Professional University, Phagwara, 144411, Punjab, India

    Mandeep Singh & M. K. Shukla

Authors
  1. Mandeep Singh
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. M. K. Shukla
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to M. K. Shukla .

Editor information

Editors and Affiliations

  1. Machine Intelligence Research Labs, Gwalior, India

    Geetam Singh Tomar

  2. Indian Institute of Technology Indore, Indore, Madhya Pradesh, India

    Narendra S. Chaudhari

  3. Applied Computing Graduate Program, University of Vale do Rio dos Sinos, Sao Leopoldo, Rio Grande do Sul, Brazil

    Jorge Luis V. Barbosa

  4. Department of Electronics and Communication Engineering, THDC-Institute of Hydropower Engineering and Technology, Tehri, Uttarakhand, India

    Mahesh Kumar Aghwariya

Rights and permissions

Reprints and permissions

Copyright information

© 2020 Springer Nature Singapore Pte Ltd.

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Singh, M., Shukla, M.K. (2020). Intelligent and Hybrid Control Techniques for Robotic Manipulator. In: Singh Tomar, G., Chaudhari, N.S., Barbosa, J.L.V., Aghwariya, M.K. (eds) International Conference on Intelligent Computing and Smart Communication 2019. Algorithms for Intelligent Systems. Springer, Singapore. https://doi.org/10.1007/978-981-15-0633-8_150

Download citation

Publish with us

Policies and ethics

Search

Navigation