Experimental studies in nonlinear discrete-time adaptive prediction and control

Abstract

This paper presents implementation results using recently introduced discrete-time adaptive prediction and control techniques using online function approximators. We consider a process control experiment as our test bed, and develop a discrete-time adaptive predictor for liquid volume and a discrete-time adaptive controller for reference volume tracking. We use Takagi-Sugeno (TS) fuzzy systems as our function approximators, and for both prediction and control we investigate the use of a least-squares update of the fuzzy system's parameters