Hysteresis Compensation Control Algorithm Comparison for the Magnetostrictive Actuators
In the applications of the magnetostrictive actuators (MA), hysteresis of the MA is particularly significant and causes undesired effect in the control system. In order to reduce hysteresis effect and obtain precision position in actual application, a neural network supervisory control was proposed and three different neural networks were used. Hysteresis would be compensated and the precision control of the MA would be obtained. These neural networks were respectively radial basis function neural network (RBFNN), dynamic recurrent neural network (DRNN) and cerebellar model articulation controller (CMAC). Through comparing these neural networks in tracking performance, tracking speed and parameter number, the control performance is better when using DRNN and CMAC, especially CMAC with 4 parameters.