Robust Nonlinear Identification Without A Priori Model Form Assumptions

A robust nonlinear identification technique, based on te Minimum Model Error (MME) optimal estimation approach, is modified by a post-etimation correlation procedure to essentially eliminate any requirement of the user to assume the form of the nonlinear model, in contrast to existing nonlinear identification approaches which usually require detailed assumptions of the nonlinearities. Model form is determined via statistical correlation of the MME optimal state estimates with the MME optimal model eror estimates. The example illustrations indicate that the method is robust with respect to prior ignorance of the model, and with respect to measurement noise, measurement frequency, and measurement record length.