IDENTIFICATION AND ADAPTIVE CONTROL OF WIENER TYPE NONLINEAR PROCESSES

Abstract

This study is concerned with a problem of the on-line identification and adaptive control of Wiener type nonlinear processes. It is assumed that the linear dynamic part of the process can be approximated by a pulse transfer function model of a known order and stable inverse followed by a known pure time-delay. The static nonlinearity is assumed to be monotonical function of its argument and it is approximated by a piecewise-po1ynomia 1 function model where the number of intervals, polynomial orders and the breakpoints should be chosen a priori. The output signal from the linear part is not available for measurement. The combined model is nonlinear in its parameters and it is characterized by a larger number of parameters than the original Wiener model. Deterministic globally stable recursive parameter identification algorithms and the model reference adaptive control systems are developed for the Wiener process model. The asymptotic properties in the presence of the input and output measurement noises and the influence of the error due to the approximation of a static nonlinearity by a piecewisepolynomial function of a finite order on the convergence properties of the proposed identification schemes are analysed. Finally, the on-line identification and adaptive control of the pH-process in a continuous flow reactor were performed. The simulation and the experimental test results are presented.