论文信息 - Hysteresis compensation for giant magnetostrictive actuators using dynamic recurrent neural network

Hysteresis compensation for giant magnetostrictive actuators using dynamic recurrent neural network

According to the hysteresis characteristics of the giant magnetostrictive actuator (MA), a dynamic recurrent neural network (DRNN) is constructed as the inverse hysteresis model of the MA, and an on-line hysteresis compensation control strategy combining the DRNN inverse compensator and a proportional derivative (PD) controller is used for precision position tracking of the MA. Simulation results validate the excellent performances of the proposed strategy

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