Abstract – The Recursive Least Squares (RLS) is usually utilized in control
applications as in self-tuning strategy to estimate the plant discrete-time transfer
function. Furthermore, it can be used as a tool to continuously monitoring the operating
condition of the plant under control. However, in such applications, the RLS should be
always in a “wake up” state so that it can estimate, in a few sampling time, the plant
transfer function after any abrupt change in its dynamic.
In this work, two modifications to the standard RLS are presented. The first
modification is called the “switching forgetting factor” while the other is called the”
resetting covariance matrix”. The two modifications are applied, under LabVIEW
environment, on-line to estimate the proper transfer function of a DC motor as an
example to show their capabilities to monitor the motor operation. It is found that with
these modifications, the RLS can estimate the plant transfer function much faster in
comparison to the standard RLS algorithm.