http://iet.metastore.ingenta.com
1887

Friction and uncertainty compensation of robot manipulator using optimal recurrent cerebellar model articulation controller and elasto-plastic friction observer

Friction and uncertainty compensation of robot manipulator using optimal recurrent cerebellar model articulation controller and elasto-plastic friction observer

For access to this article, please select a purchase option:

Buy article PDF
$19.95
(plus tax if applicable)
Buy Knowledge Pack
10 articles for $120.00
(plus taxes if applicable)

IET members benefit from discounts to all IET publications and free access to E&T Magazine. If you are an IET member, log in to your account and the discounts will automatically be applied.

Learn more about IET membership 

Recommend Title Publication to library

You must fill out fields marked with: *

Librarian details
Name:*
Email:*
Your details
Name:*
Email:*
Department:*
Why are you recommending this title?
Select reason:
 
 
 
 
 
IET Control Theory & Applications — Recommend this title to your library

Thank you

Your recommendation has been sent to your librarian.

A model-free control scheme with the elasto-plastic friction observer is presented for robust and high-precision positioning of a robot manipulator. The traditional model-based adaptive controller requires information on the robotic dynamics in advance and thus undergoes robustness problem because of complex dynamics and non-linear frictions of a robot system. This problem is overcome by an employed model-free recurrent cerebellar model articulation controller (RCMAC) system and friction estimator for friction and uncertainty compensation of a robot manipulator. The adaptive laws of the RCMAC networks to approximate an ideal equivalent sliding mode control law and adaptive friction estimation laws based on the elasto-plastic friction model are derived based on the Lyapunov stability analysis. To guarantee stability and increase convergence speed of the RCMAC network, the optimal learning rates are obtained by the fully informed particle swarm (FIPS) algorithm. The robust positioning performance of the proposed control scheme is verified by simulation and experiment for the Scorbot robot in the presence of the joint dynamic friction and uncertainty.

References

    1. 1)
      • Dahl, D.: `A solid friction model', TOR-0158, Aerosp Corp EL Segundo CA Technical Report, 1968, p. 3107–3118.
    2. 2)
    3. 3)
    4. 4)
    5. 5)
    6. 6)
    7. 7)
    8. 8)
    9. 9)
    10. 10)
    11. 11)
    12. 12)
    13. 13)
    14. 14)
    15. 15)
    16. 16)
    17. 17)
      • L.X. Wang . (1994) Adaptive fuzzy systems and control: design and stability analysis.
    18. 18)
    19. 19)
    20. 20)
    21. 21)
    22. 22)
    23. 23)
    24. 24)
    25. 25)
    26. 26)
      • V.I. Utkin . (1992) Sliding modes in control and optimization.
    27. 27)
      • J.J.E. Slotine , W. Li . (1991) Applied nonlinear control.
    28. 28)
      • Eberhart, R., Kennedy, J.: `A new optimizer using particle swarm theory', Proc. Sixth Int. Symp. on Micro Machine and Human Science, MHS’95, 1995, p. 39–43.
    29. 29)
    30. 30)
    31. 31)
      • (2006) MF 624 multifunction I/O card manual.
http://iet.metastore.ingenta.com/content/journals/10.1049/iet-cta.2010.0389
Loading

Related content

content/journals/10.1049/iet-cta.2010.0389
pub_keyword,iet_inspecKeyword,pub_concept
6
6
Loading
This is a required field
Please enter a valid email address