Adaptive inverse modeling of a shape memory alloy wire actuator and tracking control with the model
It is well known that the Preisach model is useful to approximate the effect of hysteresis behavior in smart materials, such as piezoactuators and Shape Memory Alloy(SMA) wire actuators. For tracking control, many researchers estimate a Preisach model and then compute its inverse model for hysteresi...
| Main Author: | |
|---|---|
| Other Authors: | |
| Format: | Others |
| Language: | en_US |
| Published: |
2010
|
| Subjects: | |
| Online Access: | http://hdl.handle.net/1969.1/ETD-TAMU-1713 http://hdl.handle.net/1969.1/ETD-TAMU-1713 |
| Summary: | It is well known that the Preisach model is useful to approximate the effect of
hysteresis behavior in smart materials, such as piezoactuators and Shape Memory
Alloy(SMA) wire actuators. For tracking control, many researchers estimate a Preisach
model and then compute its inverse model for hysteresis compensation. However, the
inverse of its hysteresis behavior also shows hysteresis behavior. From this idea, the
inverse model with Kransnoselskii-Pokrovskii(KP) model, a developed version of
Preisach model, can be used directly for SMA position control and avoid the inverse
operation. Also, we propose another method for the tracking control by approximating
the inverse model using an orthogonal polynomial network. To estimate and update the
weight parameters in both inverse models, a gradient-based learning algorithm is used.
Finally, for the SMA position control, PID controller, adaptive controllers with KP
model and adaptive nonlinear inverse model controller are compared experimentally. |
|---|