Wiener model based nonlinear predictive control

This paper addresses the problem of discrete-time nonlinear predictive control of Wiener systems. Wiener-model-based nonlinear predictive control combines the advantages of lineal-model-based predictive control and gain scheduling while retaining a moderate level of computational complexity. A clear relation is shown between an iteration in the optimization of the nonlinear control problem and the control problem of the underlying linear-model-based method. This relation has a simple form of gain scheduling, thus the properties of the nonlinear control system can be analysed from the comprehensible linear control aspect. Several disturbance rejection techniques ale proposed and compared. The method was tested on a simulated model of a pH neutralization process. The performance was excellent also in the case of a considerable plant-to-model mismatch. The method can be applied as a first next step in cases where the performance of linear control is unsatisfactory owing to process nonlinearity.