Recursive prediction error identification using the nonlinear Wiener model
Automatica (Journal of IFAC) - Special section on fault detection, supervision and safety for technical processes
Frequency-sampling filters: an improved model structure for step-response identification
Automatica (Journal of IFAC)
Recursive subspace identification of linear and non-linear Wiener state-space models
Automatica (Journal of IFAC)
Frequency domain identification of Wiener models
Automatica (Journal of IFAC)
Nonparametric identification of Wiener systems
IEEE Transactions on Information Theory
Design of a fuzzy controller for pH using genetic algorithm
ICS'05 Proceedings of the 9th WSEAS International Conference on Systems
Gustafson-Kessel (G-K) clustering approach of T-S fuzzy model for nonlinear processes
CCDC'09 Proceedings of the 21st annual international conference on Chinese Control and Decision Conference
Control relevant identification for robust optimal control
Automatica (Journal of IFAC)
Hi-index | 22.15 |
This paper presents an approach to the identification of time-varying, nonlinear pH processes based on the Wiener model structure. The algorithm produces an on-line estimate of the titration curve, where the shape of this static nonlinearity changes as a result of changes in the weak-species concentration and/or composition of the process feed stream. The identification method is based on the recursive least-squares algorithm, a frequency sampling filter model of the linear dynamics and a polynomial representation of the inverse static nonlinearity. A sinusoidal signal for the control reagent flow rate is used to generate the input-output data along with a method for automatically adjusting the input mean level to ensure that the titration curve is identified in the pH operating region of interest. Experimental results obtained from a pH process are presented to illustrate the performance of the proposed approach. An application of these results to a pH control problem is outlined.