Control system design automation with robust tracking thumbprint performance using a multi-objective evolutionary algorithm

Abstract

This paper develops a multi-objective evolutionary based methodology for control system design automation of robust tracking thumbprint performances in QFT. Unlike conventional two-stage design approach, the technique is capable of evolving both nominal controller and pre-filter concurrently without the need of QFT bound computation and manual loop shaping procedure. It is shown that the method can easily accommodate practical soft/hard constraints and allows engineers to examine the different design trade-offs. Validation upon a benchmark QFT design problem illustrates the usefulness of the proposed methodology.