Robust adaptive control using multiple models, switching and tuning

Access Full Text

Robust adaptive control using multiple models, switching and tuning

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

Buy article PDF
£12.50
(plus tax if applicable)
Add to cart
Buy Knowledge Pack
10 articles for £75.00
(plus taxes if applicable)
Add to cart

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 Institution of Engineering and Technology
Published

The supervisory control problem is analysed as an online robust design problem using switching to select the relevant models for designing the control law. The proposed supervisory control algorithm is based on the integration of concepts used in supervisory adaptive control, robust control and receding horizon control. It involves a two-stage adaptive control algorithm: (i) the identification of a time-varying set of models ℒ(k), from the set of admissible models ℒ, that explains the input–output behaviour of the system, followed by (ii) the design of the control law using a parametric linear optimisation problem. The authors show that under the proposed supervisory control algorithm, the system output remains bounded for any bounded disturbance. The use of superstability concepts, together with certain assumptions on ℒ, allows us to establish overall performance and robust stability guarantees for the supervisory scheme and to include constrains in the closed-loop variables as well as in the controller structure. The relevant features of the proposed control algorithm are demonstrated in a numerical simulation.

Inspec keywords: adaptive control; robust control; linear programming; control system synthesis; closed loop systems

Other keywords: supervisory adaptive control; robust stability; admissible model; two-stage adaptive control; control tuning; control switching; robust adaptive control; receding horizon control; closed-loop variable; robust design problem; superstability concept; supervisory control problem; parametric linear optimisation problem

Subjects: Optimisation techniques; Stability in control theory; Control system analysis and synthesis methods; Self-adjusting control systems

References

    1. 1)
      • K.S. Narendra , A.M. Annaswamy . (1989) Stable adaptive systems.
    2. 2)
      • Halpern, M., Polyak, B.: `Optimal tracking with fixed order controllers', Proc. 39th IEEE Conf. on Decision Control, 2000, 4, p. 3908–3913.
    3. 3)
    4. 4)
    5. 5)
    6. 6)
    7. 7)
      • F. Schweppe . (1973) Uncertain dynamic systems.
    8. 8)
      • B. Anderson . Failures of adaptive control theory and their resolution. Commun. Inf. Syst. , 1 , 1 - 20
    9. 9)
    10. 10)
    11. 11)
      • C.G. Goodwin , K.S. Sin . (1984) Adaptive filtering prediction and control.
    12. 12)
      • K.J. Astrom , B. Wittenmark . (1995) Adaptive control.
    13. 13)
      • Rosa, P., Shamma, J., Silvestre, C., Athans, M.: `Integration of the stability overlay (so) with the robust multiple-model adaptive control (RMMAC)', Proc. 17th Mediterranean Conf. Automation and Control, 2009, p. 223–228.
    14. 14)
    15. 15)
    16. 16)
      • Rosa, P., Silvestre, C., Shamma, J., Athans, M.: `Multiple-model adaptive control with set-valued observers', Proc. 48th IEEE Conf. on Decision and Control, 2009.
    17. 17)
    18. 18)
      • Wang, R., Safonov, M.: `Stability of unfalsified adaptive control using multiple controllers', Proc. American Control Conf., 2005, p. 3162–3167.
    19. 19)
      • R. Horn , C. Johnson . (1985) Matrix analysis.
    20. 20)
      • P. Ioannou , J. Sun . (1996) Robust adaptive control.
    21. 21)
    22. 22)
      • V. Chankong , Y. Haimes . (1983) Multiobjective decision making: theory and methodology.
    23. 23)
      • C.A. Desoer , M. Vidyasagar . (1975) Feedback systems: input–output properties.
    24. 24)
      • R. Bellman . (1970) Introduction to matrix analysis.
    25. 25)
      • Dehghani, A., Anderson, B., Lanzon, A.: `Unfalsified adaptive control: a new controller implementation and some remarks', Proc. 2007 European Control Conf., 2007, p. 709–716.
    26. 26)
    27. 27)
    28. 28)
    29. 29)
    30. 30)
    31. 31)
    32. 32)
    33. 33)
    34. 34)
    35. 35)
    36. 36)
      • Miyasato, Y.: `Model reference adaptive control of polytopic LPV systems an alternative approach to adaptive control', IEEE Int. Conf. on Control Applications, 2006, p. 2012–2017.
    37. 37)
      • Rosa, P., Shamma, J., Silvestre, C., Athans, M.: `Stability overlay for adaptive control laws applied to linear time-invariant systems', Proc. American Control Conf., 2009, p. 1934–1939.
    38. 38)
      • E. Kaszkurewicz , A. Bhaya . (1999) Matrix diagonal stability in systems and computation.
    39. 39)
    40. 40)
    41. 41)
    42. 42)
      • Hespanha, J.: `Tutorial on supervisory control', Lecture Notes for the Workshop Control using Logic and Switching for the 40th Conf. Decision and Control, 2001, p. 1–46.
    43. 43)
http://iet.metastore.ingenta.com/content/journals/10.1049/iet-cta.2010.0550
Loading

Related content

content/journals/10.1049/iet-cta.2010.0550
pub_keyword,iet_inspecKeyword,pub_concept
6
6
Loading