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

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

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  • Author(s): N.A. Wahab 1 ; R. Katebi 2 ; J. Balderud 3 ; M.F. Rahmat 1
    • View affiliations
    • Affiliations: 1: Department of Control and Instrumentation Engineering, Faculty of Electrical Engineering, Universiti Teknologi Malaysia, Johor, Malaysia
      2: Industrial Control Centre, Department of Electronic and Electrical Engineering, University of Strathclyde, Glasgow, UK
      3: Industrial Control Centre, Department of Electronic and Electrical Engineering, Outokumpu Stainless AB, Degerfors, Sweden
  • Source: Volume 5, Issue 6, 14 April 2011, p. 803 – 812
    DOI:  10.1049/iet-cta.2010.0068 , Print ISSN 1751-8644, Online ISSN 1751-8652
© The Institution of Engineering and Technology
Published

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.

Inspec keywords: optimisation; control system synthesis; wastewater; predictive control; singular value decomposition; adaptive control

Other keywords: wastewater systems; subspace identification technique; data-driven indirect adaptive model-based predictive control; receding horizon approach; suboptimal SVD-based optimisation technique; QR decomposition; singular value decomposition; controller design; controller parameter direct identification

Subjects: Natural resources and environmental control; Algebra; Optimisation techniques; Optimal control; Self-adjusting control systems; Control system analysis and synthesis methods

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