Modified on-line rls identification for condition monitoring

Abstract EN

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.