Adaptive predictive functional control based on Takagi-Sugeno model and its application to pH process

Abstract

In order to obtain accurate prediction model and compensate for the influence of model mismatch on the control performance of the system and avoid solving nonlinear programming problem, an adaptive fuzzy predictive functional control (AFPFC) scheme for multivariable nonlinear systems was proposed. Firstly, multivariable nonlinear systems were described based on Takagi-Sugeno (T-S) fuzzy models; assuming that the antecedent parameters of T-S models were kept, the consequent parameters were identified on-line by using the weighted recursive least square (WRLS) method. Secondly, the identified T-S models were linearized to be time-varying state space model at each sampling instant. Finally, by using linear predictive control technique the analysis solution of the optimal control law of AFPFC was established. The application results for pH neutralization process show that the absolute error between the identified T-S model output and the process output is smaller than 0.015; the tracking ability of the proposed AFPFC is superior to that of non-AFPFC (NAFPFC) for pH process without disturbances, the overshoot of the effluent pH value of AFPFC with disturbances is decreased by 50% compared with that of NAFPFC; when the process parameters of AFPFC vary with time the integrated absolute error (IAE) performance index still retains to be less than 200 compared with that of NAFPFC.