Abstract: A matching control method for mechanical impedance of a magnetostrictive precision transducer includes developing a three-layer neural network model corresponding to a Young's modulus of a Terfenol-D material; acquiring sample data to form a training sample set and a testing sample set; training the model using a Bayesian regularization training algorithm, and optimizing connection weights and thresholds among layers of the tested model, so as to obtain a final three-layer neural network model; based on the final model, building an inverse model of mechanical impedance of the magnetostrictive precision transducer; using a current level of impedance of a load as an input of the inverse model to obtain a bias magnetic field, and changing a level of the bias magnetic field by changing a bias current in an excitation coil of the transducer, thereby achieving adaptive matching between the mechanical impedance of the transducer and the impedance of the load.

Abstract: A matching control method for mechanical impedance of a magnetostrictive precision transducer includes developing a three-layer neural network model corresponding to a Young's modulus of a Terfenol-D material; acquiring sample data to form a training sample set and a testing sample set; training the model using a Bayesian regularization training algorithm, and optimizing connection weights and thresholds among layers of the tested model, so as to obtain a final three-layer neural network model; based on the final model, building an inverse model of mechanical impedance of the magnetostrictive precision transducer; using a current level of impedance of a load as an input of the inverse model to obtain a bias magnetic field, and changing a level of the bias magnetic field by changing a bias current in an excitation coil of the transducer, thereby achieving adaptive matching between the mechanical impedance of the transducer and the impedance of the load.