On-line set-point optimisation and predictive control using neural Hammerstein models

This paper discusses a computationally efficient approach to set-point optimisation which cooperates with predictive control and its application to a multivariable neutralisation reactor. In the presented system structure a neural Hammerstein model of the process is used. For set-point optimisation, a linearisation of the steady-state model derived from the neural Hammerstein model is calculated on-line. As a result, the set-point is determined from a linear programming problem. For predictive control, a linear approximation of the neural Hammerstein model is calculated on-line and the control policy is determined from a quadratic programming problem. Thanks to linearisation, the necessity of on-line nonlinear optimisation is eliminated. This article emphasises advantages of neural Hammerstein models: accuracy, a limited number of parameters and a simple structure. Thanks to using such models, model transformations can be carried out very efficiently on-line. It is demonstrated that results obtained in the presented structure are very close to those achieved in a computationally demanding structure with on-line nonlinear optimisation. It is also shown that for the considered neutralisation reactor the classical system structure in which for control and set-point optimisation linear models are used gives numerically wrong results.