Data-driven adaptive model-based predictive control with application in wastewater systems

Data-driven adaptive model-based predictive control with application in wastewater systems

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

Buy article PDF
(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
Your details
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.

This study is concerned with the development of a new data-driven adaptive model-based predictive controller (MBPC) with input constraints. The proposed methods employ subspace identification technique and a singular value decomposition (SVD)-based optimisation strategy to formulate the control algorithm and incorporate the input constraints. Both direct adaptive model-based predictive controller (DAMBPC) and indirect adaptive model-based predictive controller (IAMBPC) are considered. In DAMBPC, the direct identification of controller parameters is desired to reduce the design effort and computational load while the IAMBPC involves a two-stage process of model identification and controller design. The former method only requires a single QR decomposition for obtaining the controller parameters and uses a receding horizon approach to process input/output data for the identification. A suboptimal SVD-based optimisation technique is proposed to incorporate the input constraints. The proposed techniques are implemented and tested on a fourth order non-linear model of a wastewater system. Simulation results are presented to compare the direct and indirect adaptive methods and to demonstrate the performance of the proposed algorithms.


    1. 1)
    2. 2)
    3. 3)
    4. 4)
      • Van Helvoort, J.J.: `Unfalsified control: data-driven control design for performance improvement', 2007, , Technische Universiteit Eindhoven.
    5. 5)
      • Kostic, D.: `Data-driven robot motion control design', 2004, , Technische Universiteit Eindhoven.
    6. 6)
      • K.J. Astrom , B. Wittenmark . (1995) Adaptive control.
    7. 7)
      • Favoreel, W., De Moor, B., Van Overschee, P., Gevers, M.: `Model-free subspace based LQG design', Proc. American Control Conf., 1998, San Diego, CA.
    8. 8)
      • Favoreel, W., De Moor, B., Gevers, M.: `Subspace predictive control', Proc. 14th IFAC, 1999, Beijing, China.
    9. 9)
    10. 10)
      • H. Yang , S. Li . Subspace-based adaptive predictive control of nonlinear systems. Int. J. Innov. Comput., Inf. Control , 4 , 743 - 753
    11. 11)
      • Ruscio, D.D.: `Model based predictive control and identification: a linear state space model approach', Proc. 36th Conf. on Decision and Control, 1997b, San Diego, CA.
    12. 12)
    13. 13)
    14. 14)
    15. 15)
    16. 16)
    17. 17)
      • De Moor, B.: `Mathematical concepts and techniques for modelling of static and dynamic systems', 1988, PhD, Katholieke Univeriteit Leuven, Department of Electrical Engineering, Belgium.
    18. 18)
    19. 19)
      • P.V. Overschee , B. De Moor . (1996) N4SID: Subspace identification for linear systems: theory, implementation, applications.
    20. 20)
      • G.H. Golub , C.F. Van Loan . (1989) Matrix computation.
    21. 21)
    22. 22)
    23. 23)
      • M. Johansson . (2003) Piecewise linear control systems: a computational approach.

Related content

This is a required field
Please enter a valid email address