Establishment and experimental verification of a prandtl–ishlinskii hysteresis model for tri-layer conducting polymer actuators

In this paper, a Prandtl–Ishlinskii hysteresis

model (PI) is used to build a rate-independent hysteresis model

for a class of conducting polymer actuators typified by tri-layer

conjugated polymer actuators. Firstly, an off-line method is

proposed to identify a discretization density function for the

hysteresis model, and then a linear transfer function for the

actuator is identified using the PI inverse model. Secondly, a

neural network approach is proposed to realize an adaptive

on-line identification method for the density function of the PI

hysteresis model. In the back propagation (BP) algorithm for

the neural network, the discretization PI operator is considered

as an operational function of the neural network and the density

function is considered as the power value. Finally, the

simulation and experimental results are presented to

demonstrate the validity of the model identification method and

the actuator model.