Simulating rotary-table neural net controller research based on double feedback structure

The three-axis electronic-hydraulic simulating rotary-table is an important equipment for ground experimentation and simulation of battleplanes and ballistic missiles. In order to drive huge loads, the rotary-table adopts double hydraulic motors for the outer gimbal. However the double motors' non-synchronization, friction and laden disturbance are three main factors that could arouse the outer gimbal's local torsion and shock. It will decrease the tracking accuracy and dynamic performance of the simulating rotary-table. So for this rotary-table the Proportion Integral Differential (PID) controller could hardly achieve high accuracy under the above three factors' reciprocity and interrelationship. This paper elaborates building a velocity-position double feedback system for this rotary-table, and designing Predictive Neural Net (PNN)-Fuzzy Neural Net(FNN) controller for the double feedback control system. The simulating results of PNN-FNN controller has shown that the proposed approach can achieve high displacement tracking accuracy and dynamic performance when the loads of the rotary-table outer gimbal changes or two hydraulic motors' rotate speeds differs greatly.