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

Adaptive position and trajectory control of autonomous mobile robot systems with random friction

Adaptive position and trajectory control of autonomous mobile robot systems with random friction

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.

The authors present a robust adaptive control approach using model reference adaptive control (MRAC) for autonomous robot systems with random friction. First, a non-linear model of the robot system is approximated by feedback linearisation to derive a nominal control law. Next, a least square observer is constructed for the online estimation of friction dynamics. The authors derive a perturbed system model governing the friction estimation error and design an MRAC control to mitigate its effect. Also, stability conditions for the perturbed system model using the Lyapunov stability theory are derived. The authors demonstrate the success of the proposed control methodology through computer simulation, including a comparison to a traditional controller based on nominal dynamics.

References

    1. 1)
      • Harter, D.: `Evolving neurodynamic controllers for autonomous robots', Proc. Int. Joint Conf. on Neural Networks, July 2005, Montreal, Canada, p. 137–142.
    2. 2)
      • Ohnishi, T., Asakura, T.: `Autonomous walking velocity control strategy for a spider-robot based on biological approach', Proc. SICE Conf., August 2005, Okayama, Japan, p. 382–387.
    3. 3)
    4. 4)
      • Nikkhah, M., Ashrafiuon, H., Muske, K.R.: `Optimal sliding mode control for underactuated systems', Proc. American Control Conf., June 2006, Minneapolis, USA, p. 4688–4693.
    5. 5)
      • Ramirez, J.M., Gomez-Gil, P., Larios, F.L.: `A robot-vision system for autonomous vehicle navigation with fuzzy-logic control using lab-view', Proc. Electronics, Robotics and Automotive Mechanics Conf., September 2007, Cuernavaca, Mexico, p. 295–300.
    6. 6)
    7. 7)
    8. 8)
    9. 9)
    10. 10)
      • J.M. Mendel . (1995) Lessons in estimation theory for signal processing, communications, and control.
    11. 11)
      • H.K. Khalil . (1988) Nonlinear systems.
    12. 12)
    13. 13)
http://iet.metastore.ingenta.com/content/journals/10.1049/iet-cta.2009.0251
Loading

Related content

content/journals/10.1049/iet-cta.2009.0251
pub_keyword,iet_inspecKeyword,pub_concept
6
6
Loading
This is a required field
Please enter a valid email address