This paper proposes a new approach for constrained multivariable predictive control based
on the use of a recurrent neural network as a non-linear prediction model of the plant under
control. The model is derived in a natural way from its linear counterpart, and it is a
representation of the system in the state-space form. The proposed predictive control
scheme is able to deal with constraints in the system variables and highly non-linear
dynamics. As an example model, a sulphitation tank taken from the sugar industry has been …

This paper proposes a new approach for constrained multivariable predictive control based
on the use of a recurrent neural network as a prediction model. The model is derived in a
natural way from its linear counterpart and it is a representation of the system in the state
space form. The proposed predictive control scheme is able to deal with constraints in the
system variables and highly non-linear dynamics. An example belonging to the sugar
industry, the control of a water sulfitation plant, is presented together with a comparison with …