Online hysteresis identification and compensation for piezoelectric actuators

Hysteresis in piezoelectric actuators (PEAs) causes significant position errors and limits the application of PEAs. Compensating hysteresis via inverse models is one of the most popular approaches to reduce its effects. However, the construction of an accurate model is very challenging since hysteresis is coupled with creep and high-order dynamics, and hysteresis properties are also affected by operating conditions and aging effects. To overcome these challenges, this article proposes an adaptive generalized Maxwell-slip (AGMS) algorithm to identify and compensate for hysteresis online. By evenly distributing the saturation deformations throughout the desired range, a linear relation between the output force and the spring stiffness is generated, which enables a low-computational-complexity algorithm, such as the recursive least-squares algorithm, to update the stiffness online. Since the GMS model is self-invertible, an inverse model can be further analytically constructed in real time and utilized to compensate for the hysteresis. Experimental and comparison studies are carried out. The results show that this approach relaxes the requirement on the precision of the model and is robust to the operating conditions due to the capability of updating model parameters online. The normalized root mean square tracking error is approximately 0.4%.