Tracking control of piezoelectric actuator using adaptive model

Abstract

Piezoelectric actuators (PEAs) have been widely used in micro- and nanopositioning applications due to their fine resolution, rapid responses, and large actuating forces. However, a major deficiency of PEAs is that their accuracy is seriously limited by hysteresis. This paper presents adaptive model predictive control technique for reducing hysteresis in PEAs based on autoregressive exogenous model. Experimental results show the effectiveness of the proposed method.

Adaptive feed-forward controller of piezoelectric actuator for micro/nano-positioning

“…The output position plots of the actual PEA (P841.20) and the simulation model using the same input signals with The output position of actual experimental data and the output from the simulation model using the same input signal are shown in figures 4b, 4d, 5b and 5d. The initial parameters are updated using (31) and (32). The RLSEbased adaptive FF controller is first simulated using MATLAB-SIMULINK.…”

“…An adaptive sliding mode control method is proposed in [31] for eliminating hysteresis effect and achieving highly precise output displacement of PEAs. Paper [32] presents an adaptive model predictive control technique for reducing hysteresis in PEAs based on autoregressive exogenous (ARX) model. It is inferred that an adaptive controller with adjustable specification and mechanism for adjusting parameters is preferred for piezo-actuation where the parameters can be modified to compensate for the shift in dynamics of the system and the disturbances acting on the plant.…”

High-Performance Tracking for Piezoelectric Actuators Using Super-Twisting Algorithm Based on Artificial Neural Networks

“…The replacement of the bounded condition of Equation ( 33) within the right hand-side term of Equation ( 32) results in Equation (34).…”

“…Feedback linearization with uncertainties control has been employed recently with the addition of a Bouc-Wen (BW) model for hysteresis where the researchers achieved good accuracy although the BW is limited for asymmetric hysteresis response [32,33]. An advance approach has equally been used with model predictive control based on an adaptive algorithm that was tested in a real system that provided an error of around 1%, which is an acceptable although tests were performed with low amplitude signals [34]. Robust controllers had also been investigated with adequate results; authors from Reference [35] implemented a robust control with its stability analysis of a scheme based on inverse models that produced decent outcomes as an error around 0.5 µm.…”