Recursive least squares based estimation schemes for self‐tuning control

Recursive Least Squares (RLS) is the most popular parametric identification method used for on‐line process model estimation and self‐tuning control. The basic least squares scheme is outlined in this paper and its lack of ability to track changing process parameters is illustrated and explained. Several variants of the basic algorithm which have appeared elsewhere in the literature are discussed. Some of these algorithms contain different modifications to the basic scheme which are intended to prevent this loss of alertness to changing process parameters. Other variations of the least squares algorithm are presented which attempt to deal with parameter estimation in the presence of disturbances and unmodelled process dynamics.