Modeling and control of piezoelectric actuator based on radial basis function neural network
FAN Jia-hua
MA Lei
ZHOU Pan
LIU Jia-bin
ZHOU Ke-min
Abstract：For the rate-dependent hysteresis nonlinearity of piezoelectric actuators, a Hammerstein model is established. Using a radial-basis-function (RBF) neural network to represent the hysteresis nonlinearity, an auto-regressive exogenous (ARX) model to represent the impact of frequency, and parameter identification is also accomplished. The proposed model describes the hysteresis characteristics of frequency ranged from 1 to 300 Hz of the signals, and the relative error is 1.99%～4.08%. A compound control strategy with RBF neural network feedforward inverse compensation and PI feedback is utilized for position tracking control, and the relative error less than 2.98%. Validity of the control strategy is proved by experimental results.
Keywords：rate-dependenthysteresisRBF neural networkpiezoelectric actuatorHammerstein model
Publication Date：2016-01-01
Online Publishing Date：2025-08-15(First online date of this platform, not the publication date of the document)
Pages：7( 856-862 )