Sponsored by: STW
The project aims at the development of methods that enable to transfer the high investment return of currently used Model-based Predictive Controller (MPC) schemes for linear systems to important classes of nonlinear systems in the process industry.
The special way in which the nonlinearity enters the Wiener model can be exploited by transforming it into uncertainty. The result will be an uncertain linear model, which enables to use robust linear MPC techniques. A similar approach can be applied for Hammerstein systems, in which case a linear dynamic block is preceded by a static input nonlinearity. This Hammerstein-Wiener MPC algorithm  extends the linear MPC algorithm described in . A case study, concerning the distillation column benchmark, has demonstrated the effectiveness of the proposed Wiener MPC algorithm and is presented in .
Also discrete-time bilinear models may be useful for black-box identification of nonlinear processes. In bilinear models the nonlinearity enters the dynamic part of the model, i.e. the state equation contains a product term between the current state and the current input. This property can be exploited for solving a ''classical'' finite horizon MPC problem . An application of bilinear MPC to a polymerization reactor is presented in . Extensions to an infinite-horizon bilinear MPC algorithm can be found in [5,11]. Extensions to bilinear MPC algorithms that aim at a low computational demand for the on-line computations are reported in [6,5].
This project is part of STW project DEL 55.3891. This project is done in co-operation with the Control group of the University of Oxford, UK.
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Last modified: 24 March 2005, 10:16 UTC
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