Universiti Teknologi Malaysia Institutional Repository

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

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.

Full text not available from this repository.

Official URL: http://dx.doi.org/10.1049/iet-cta.2010.0068

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.

Item Type:Conference or Workshop Item (Paper)
Subjects:T Technology > TJ Mechanical engineering and machinery
Divisions:Electrical Engineering
ID Code:23901
Deposited By: Liza Porijo
Deposited On:18 Jun 2012 04:01
Last Modified:18 Jun 2012 04:01

Repository Staff Only: item control page