Deep Neural Network System Identification for Servomechanism System

This paper presents a systematic technique for designing the input signal to identify the one-stage servomechanism system. Sources of nonlinearities such as friction and backlash consider an obstacle to obtaining an accurate model. Also, most such systems suffer from a lack of system parameters data. So, this study establishes a model using the black-box modeling approach; simulations are performed based on real-time data collected by LabVIEW software and processed using MATLAB System Identification toolbox. The input signal for the servomechanism system driver is a pseudo-random binary sequence that considers violent excitation in the frequency interval and the output signal is the corresponding stage speed measured by rotary encoder. The candidate models were obtained using linear least squares, nonlinear least squares, and Deep Neural Network (DNN). The validation results proved that the identified model based on DNN has the smallest mean square errors compared to other candidate models.