Design and research of electronic-hydraulic rotary-table

Friction, disturbance and non-synchronization are main problems of three gimbals Rotary-Table. This paper adopt Fuzzy neural net (FNN) to deal with double hydraulic motors velocity feedback and Predictive neural net (PNN) for outer gimbal angle feedback, FNN controller simulate cerebra neural net (CNN) based on experiential control and PNN controller depend on self-study simulate CNN. FNN controller include master-slave and equation, two models switch for different state, improve system synchronization. PNN controller select nonlinear auto regressive moving average (NARMA) for system identification. Emulational results of PNN-FNN controllers show that the proposed approach can achieve high displacement tracking accuracy and dynamic performance when the loads of the simulator are changeable or two hydraulic motors' rotate speeds are different greatly.