Adaptive forgetting in recursive identification through multiple models†

Abstract A new recursive identification method, adaptive forgetting through multiple models (AFMM) is presented and evaluated using computer simulations. AFMM is especially suited for identification of systems with jumping or rapidly changing parameters. It can be viewed as a particular way of implementing adaptive gains or adaptive forgetting factors for recursive identification. The new method essentially consists of multiple recursive least-squares (RLS) algorithms running in parallel, each with a corresponding weighting factor. The simulations indicate that AFMM is able to track rapidly changing parameters well, and that the method is robust in several respects.