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

N.A. Wahab, M.R. Katebi, J. Balderud, M.F. Rahmat

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42 Citations (Scopus)
108 Downloads (Pure)


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.
Original languageEnglish
Pages (from-to)803-812
Number of pages10
JournalIET Control Theory and Applications
Issue number6
Publication statusPublished - 14 Apr 2011


  • adaptive system
  • predictive control
  • wastewater systems
  • control systems
  • adaptive control
  • wastewater
  • singular value decomposition
  • optimisation
  • control system synthesis


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