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

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

PDF (strathprints027489.pdf)
Accepted Author Manuscript

Download (1MB)| Preview


    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.