High-precision control of giant magnetostrictive actuator based on CMAC neural network

In order to compensate its inherent hysteresis nonlinearity and improve its precision of a giant magnetostrictive actuator(GMA),a real-time hysteretic compensation control strategy was proposed,combining a feedforward cerebellar model articulation controller(CMAC) and a proportional integral derivative(PID) feedback controller to realize the precision position tracking control of the GMA.As CMAC neural network could not be used to approximate the multi-valued mapping of an inverse hysteresis directly,an inverse hysteretic operator was proposed to transform the multi-valued mapping into a one-to-one mapping which could enable neural networks to approximate the behavior of an inverse hysteresis.Simulation results showed that the proposed control strategy can adapt itself to changes of hysteretic characteristics of a GMA under different input reference signals,and an on-line inverse hysteresis model of a GMA can be obtained,thus the hysteretic impact can be eliminated and high precision control of a GMA can be achieved.