Predictive Control Based on a Fuzzy Model

Škrjanc, Igor; Kavšek-Biasizzo, Katarina; Matko, Drago

doi:10.1007/978-3-7908-1886-4_13

Cited by 2 publications

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“…If the literature of model-based predictive control is studied, the most conspicuous feature is that the choice of an adequate (sometimes nonlinear) model has a distinguished role in the control design procedure of these controllers. The solutions range from the application of impulse response and step response models , to the employment of neural network models , and fuzzy models , or recently to Hammerstein models , or ARMA and CARIMA models. , The main improvements in the models, yielded with time, consist in their nonlinear feature.…”

Section: Introductionmentioning

confidence: 99%

Convolution Model Based Predictive Controller for a Nonlinear Process

Bódizs

Szeifert

Chován

1998

Ind. Eng. Chem. Res.

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The model predictive control (MPC) of a distributed parameter nonlinear laboratory heating system is studied. A nonlinear convolution model consisting of a linear dynamic and a nonlinear steady-state part is applied as the model of the process in the MPC algorithm. The dynamic part is represented by a relative impulse response model (IRM). The steady-state gain is derived from the first principle model of the system. The application of this special convolution model is as simple as the use of the transfer function model; however, it is valid on the whole operating range. MPC algorithms employing different models of the process are compared by simulation and physical tests.

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