FIT - Filtering and Identification Tool

Abstract

The program "Filtering and Identification Tool" designed for MATLAB provides an easy to handle tool for linear system identification of continuous time domain systems. But also discrete time models may be identified. The only property the process model must fulfill is that it has to be linear in the parameters. A graphical user interface guides the research engineer through all steps. As most of the time consuming algorithms are implemented as C-functions, the identification even with a huge amount of data takes only few seconds. To identify continuous time process models, derivatives of the input and output signals are required. However, these signals often cannot be measured. Therefore digital filters such as state variable filter (SVF) and differentiating finite impulse response (FIR) filters are integrated into FIT to provide the necessary derivatives. For the identification task various recursive parameter estimation methods like recursive least means squares (RLS), discrete square root filter in information form (DSFI), normalized least means squares (NLMS), etc. are included, too. Recursive algorithms with exponential fading memory (variable step size in case of NLMS) were chosen in order to identify time variant systems. But also for the offline design of real-time fault detection or adaptive control schemes using parameter estimation methods a variable forgetting factor is important to be able to track varying parameters. Thus, the main advantage of the Filtering and Identification Tool in comparison to existing software tools for system identification is the integration of both parameter estimation methods and differentiating filters.