Influence of Sampling Rate and Discretization Methods in the Parameter Identification of Systems with Hysteresis

Abstract

Hysteresis is a nonlinear behaviour, which has been considered very hard to model. It is commonly found in actuators and sensors, involving quasi-static memory effects between input and output variables. Usually, continuous time models are used to model this feature. However, polynomial NARX model has come up as an alternative to model this behaviour. Since NARX models are discrete-time models, it is important to verify how the sampling rate interfere in obtaining the mathematical model. Further, frequently continuous-time models are used as a bench test, to generate data for identification of several nonlinear behaviour, including hysteresis. This paper investigates how the sampling rate and discretization methods affects the parameter identification of a NARX model for a system with hysteresis. Improved Euler and fourth order Runge-Kutta methods are applied in a Bouc-Wen model for a magneto-rheological damper, which is used as a system to be identified by a NARX model, considering the above mentioned scenario. Least-square based technique is used in this work to estimate model parameters.