Evolutionary system identification in the time domain

Abstract

This paper develops a genetic algorithm based technique that may be used to identify multivariable system identification directly from plant step response data. Using this technique, globally optimized models for linear and non-linear systems can be identified without the need for a differentiable cost function or linearly separable parameters. Results are validated against a benchmark identification problem and a laboratory test-rig for continuous and discrete-time systems.