Generalized predictive control with a non-linear autoregressive model

Authors:
Hynek Vychodil;Michal Schmidt;Petr Nepevný;Petr Pivoňka
Affiliations:
Department of Control and Instrumentation, FEEC, VUT Brno, Brno, Czech Republic;Department of Control and Instrumentation, FEEC, VUT Brno, Brno, Czech Republic;Department of Control and Instrumentation, FEEC, VUT Brno, Brno, Czech Republic;Department of Control and Instrumentation, FEEC, VUT Brno, Brno, Czech Republic
Venue:
ACMOS'05 Proceedings of the 7th WSEAS international conference on Automatic control, modeling and simulation
Year:
2005

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Abstract

This paper presents a solution to computation of predictive control using non-linear auto-regressive models. For the non-linear model a neural network is used as a perspective tool for modelling of dynamic systems. However, the described approach is applicable to any type of auto-regressive model. The model is not linearized in the operating point, but in each control optimization step the model's derivative is computed (linearization) for all points in the prediction horizon. The method can be used in real-time control. This is verified by porting the algorithm directly to the PLC.