  Temperature control is a complex task due to its often unknown dynamics and
disturbances. This paper explores the use of Neural Nonlinear AutoRegressive
eXogenous (NNARX) models for nonlinear system identification and model
predictive control of a temperature control unit. First, the NNARX model is
identified from input-output data collected from the real plant, and a
state-space representation with known measurable states consisting of past
input and output variables is formulated. Second, a tailored model predictive
controller is designed based on the trained NNARX network. The proposed control
architecture is experimentally tested on the temperature control units
manufactured by Tool-Temp AG. The results achieved are compared with those
obtained using a PI controller and a linear MPC. The findings illustrate that
the proposed scheme achieves satisfactory tracking performance while incurring
the lowest energy cost among the compared controllers.
