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

Tools

Wahab, N.A. and Katebi, M.R. and Balderud, J. and Rahmat, M.F. (2011) Data-driven adaptive model-based predictive control with application in wastewater systems. IET Control Theory and Applications, 5 (6). pp. 803-812. ISSN 1751-8644 (https://doi.org/10.1049/iet-cta.2010.0068)

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Abstract

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.

ORCID iDs

Wahab, N.A., Katebi, M.R. ORCID logoORCID: https://orcid.org/0000-0003-2729-0688, Balderud, J. and Rahmat, M.F.;
CORE (COnnecting REpositories)