Genetic Algorithms for On-Line System Identification

Abstract

When required for batch processing, system identification can be used in a number of ways to find a good fit in terms of modelling the system structure, time delay and characteristic parameters of a plant. The best structure and delay selection can often be found in terms of a computationally simple model order testing procedure, with a range of different candidate models being considered.

Due to the need for computational efficiency for on-line identification, this is often restricted to a more straightforward parameter estimation exercise, the structure being selected as a fixed term, usually of low order. Any structural tuning is then carried out in terms of sampling period variation, which can mean that vital plant information does not appear in the identified model.

This paper presents an on-line technique for system identification, making use of genetic algorithms for structure and time delay estimation. The identification scheme is multi-level, the bottom level consisting of the more usual parameter estimation exercise, with the upper level carrying out structure identification. The first of these can update in real-time, whilst the second operates in its own time. At any instant, one model is selected as “best” in terms of both structure and parameters, and this is the one employed as on-line identification.