1. Pearson RK. Selecting nonlinear model structures for computer control. J Process Control 2003; 13: 1–26 2. Bloemen HHJ, Chou CT, van den Boom TJJ, Verdult V, Verhaegen M, Backx TC. Wiener model identification and predictive control for dual composition control of a distillation column. J Process Control 2001; 11: 601–620 3. Westwick D, Verhaegen M. Identifying MIMO Wiener systems using subsp...

It is presented in this paper a method for designing a nonlinear model predictive controller. The controller is based on a hybrid Wiener-Hammerstein fuzzy-neural predictive model and а simplified gradient optimization algorithm. The proposed approach is used to control the product temperature in a Lyophlization plant. The controller efficiency is tested and proved by simulation experiments in M...

The paper deals with parameter identification of nonlinear dynamic systems using Hammerstein-Wiener models. Multiple application of a decomposition technique provides special expressions for the model description that are linear in parameters. This allows iterative estimation of all model parameters based on the measured input/output data and estimates of internal variables. The proposed algori...

Self-tuning control with recursive identification of extremal dynamic systems is considered. The systems can be represented by combinations of linear dynamic and extremal static parts, their output being disturbed by a coloured noise. Minimumvariance controllers for Hammerstein, Wiener, and Wiener-Hammerstein-type systems are designed taking into consideration restrictions for control signal ma...

This paper analyzes the computational complexity of set membership identification of Hammerstein and Wiener systems. Its main results show that, even in cases where a portion of the plant is known, the problems are generically NP-hard both in the number of experimental data points and in the number of inputs (Wiener) or outputs (Hammerstein) of the nonlinearity. These results provide new insigh...

A new method for identifying Hammerstein/Wiener models is proposed, where the parameters of a piecewise linear characteristic and a linear transfer function are estimated by recursive least-squares (RLS). Using smooth validity functions and an adaptive algorithm for optimal partitioning of the local linear models an efficient and accurate model can be identified.

In this work we develop two different adaptive control schemes for a class of nonlinear systems. The class of systems belongs to the Hammerstein-Wiener nonlinear systems. The techniques developed are presented and an example is given in illustration. Using an approximate inverse of the nonlinear plant model, the overall system is forced to track the desired reference signal.