Publication detail

Generalized Predictive Control with a Non-linear Autoregressive Model

Hynek Vychodil, Michal Schmidt, Petr Nepevný,Petr Pivoňka

Original Title

Generalized Predictive Control with a Non-linear Autoregressive Model

English Title

Generalized Predictive Control with a Non-linear Autoregressive Model

Type

journal article - other

Language

en

Original 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.

English 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.

RIV year

2005

Released

30.03.2005

Pages from

125

Pages to

130

Pages count

6

BibTex


@article{BUT46302,
  author="Hynek {Vychodil} and Michal {Schmidt} and Petr {Nepevný} and Petr {Pivoňka}",
  title="Generalized Predictive Control with a Non-linear Autoregressive Model",
  annote="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.",
  chapter="46302",
  journal="WSEAS Transactions on Circuits",
  number="3",
  volume="2005",
  year="2005",
  month="march",
  pages="125",
  type="journal article - other"
}