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.
Original language | English |
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Pages (from-to) | 803-812 |
Number of pages | 10 |
Journal | IET Control Theory and Applications |
Volume | 5 |
Issue number | 6 |
DOIs | |
Publication status | Published - 14 Apr 2011 |
Keywords
- adaptive system
- predictive control
- wastewater systems
- control systems
- adaptive control
- wastewater
- singular value decomposition
- optimisation
- control system synthesis