Excitation signal design for generating optimal training data for complex dynamic systems

The appropriate choice of excitation signal in system identification is an important but rarely considered part of the process that determines the success of many downstream activities. This paper presents a novel methodology for excitation signal design to create high accuracy multivariable nonlinear dynamic neuro-fuzzy models. Two different approaches to experimental design are investigated. In the first, a prescribed transient manoeuvre is used. In the second, informative potential is used to deconstruct the transient into a sequence of inputs designed to cover the same input space and reduce model development time. Star discrepancy is used to evaluate the resulting designs and is shown to provide a good proxy for excitation design quality. Results are presented showing the prediction accuracy of the model in terms of an application example, achieving a minimum < 2% cumulative error over a two minute transient. It is shown that the neuro-fuzzy models identified using data from the two different approaches have similar accuracy. However, the second approach based on informative potential leads to a more generalised model and reduces the development time by a factor of four. This is a significant result that shows the importance of choosing an appropriate excitation signal.