SELF-TUNING CONTROL UNDER INPUT VARIANCE CONSTRAINT

摘要

In this paper, a variance-constrained self-tuning control is considered for a plant given by discrete-time ARMAX model. A minimization of a quadratic cost function under constraint is approached by LQG and stochastic approximation (SA) methods, as well as by MUSMAR, a predictive adaptive controller based on multiple identifiers. The optimization algorithms obtained are simulated for unstable plant model and different structures of the controller.