Continuous-time least-squares forgetting algorithms for indirect adaptive control

摘要

Many variants of least-squares forgetting algorithms can be found in the literature. They all have advantages and disadvantages with respect to adaptation rate, robustness against noise, parameter tuning and computational effort. In this paper, a general forgetting least-squares algorithm is presented, where a large number of algorithms known from the literature can be derived from this algorithm by just specifying a general weighting matrix. This also serves as the basis for the design of a novel least-squares forgetting algorithm which guarantees predefined upper and lower bounds on the gain matrix. This algorithm combines the benefits of exponential forgetting and resetting with the eigenvalue modulation of selective forgetting at low computational costs. (C) 2021 The Author(s). Published by Elsevier Ltd on behalf of European Control Association.