Abd. Wahab, Norhaliza and Katebib, Reza and Balderudc, Jonas and Rahmat, M. Fuaad (2010) Data-driven adaptive model-based predictive control with application to wastewater systems. In: IET Control Theory & Applications, 2010, n/a.
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Official URL: http://dx.doi.org/10.1049/iet-cta.2010.0068
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.
|Item Type:||Conference or Workshop Item (Paper)|
|Subjects:||T Technology > TJ Mechanical engineering and machinery|
|Deposited By:||Liza Porijo|
|Deposited On:||18 Jun 2012 04:01|
|Last Modified:||18 Jun 2012 04:01|
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