Design variable structure fuzzy control based on deep neural network model for servomechanism drive system

This paper presents a new scheme for variable structure (VS) fuzzy PD controller. The rule base of the fuzzy PD controller is tuned online. The purpose of the proposed controller is to track accurately a preselected position command for the servomechanism system. Therefore, this study establishes a model using a black-box modeling approach; simulations were performed based on real-time data collected by LabVIEW and processed using MATLAB. The input signal for the servomechanism driver is a pseudo-random binary sequence that considers violent excitation in the frequency interval. 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. Then, the DNN identified model was used to design the proposed control techniques. A comparison had been executed between the VS fuzzy PD control, the conventional PD control, and the fixed structure fuzzy PD control. The experimental results confirm the proposed VS fuzzy PD control can absorb the nonlinear behavior of the system. The speed regulation test, it reduces the rise time from 50% to 56%. While continuously changing in speed, it has the smallest tracking error (0.412 inches).