Tracking Control for Piezoelectric Actuators with Advanced Feed-Forward Compensation Combined with PI Control

Abstract

Piezoelectric actuators (PEA) are devices which can support large actuation forces compared to their small size and are widely used in high precision applications where micro-and nanopositioning is required. Nonetheless, these actuators have undeniable non-linearities were the well-known are creep, vibration dynamics and hysteresis. The latter mentioned is originated due to a combination of mechanical strain and electric field action; as a consequence, these can affect the PEA tracking performance and even reach instability. The scope of this paper is to reduce the hysteresis effect using and comparing different control strategies like feedback with feed-forward (FF) structure which is often used to compensate the non-linearities and diminish the errors due to uncertainties. In this research, black-box models were analysed; subsequently, a classic feedback control like proportional-integral (PI) was combined with the FF methods proposed separately and embedded into a dSpace platform to perform real-time experiments. Results were analysed in-depth in terms of the error, the control signal and the integral of the absolute error (IAE). It was found that with the proposed methods, the hysteresis effect could be diminished to acceptable ranges for high-precision tracking with a satisfactory control signal.

Closed-LSTM neural network based reference modification for trajectory tracking of piezoelectric actuator

“…In recent years, many researchers have applied artificial neural network (ANN) modeling and control methods to trajectory tracking of PEAs [13,14,15,16], especially the inverse compensation control based on ANN. In [17], in order to improve the performance of active vibration control for a helicopter driven by piezoelectric stack actuators (PSAs), a hysteresis neural network based on nonlinear autoregressive neural network (NARX) is established, and then another neural network is used to compensate for the hysteresis characteristics of the PSA.…”

Advanced Trajectory Control for Piezoelectric Actuators Based on Robust Control Combined with Artificial Neural Networks

“…In former investigations, we tested TDNN structures to reduce hysteresis, which showed proper results in combination with conventional controllers [33]. As the name states, a TDNN is an extension of a classic multilayer perceptron (MLP) that works with time signals.…”

Reference modification for trajectory tracking using hybrid offline and online neural networks learning

“…As mechatronic systems generally have complicated dynamics, which are influenced by uncertainties [24], there is ample motivation to investigate the effectiveness of machine learning in the control of mechatronic systems [25]. Some researchers were attracted by the excellent capabilities of deep neural networks (DNNs) in function approximation and thus revisited the idea of constructing an NN model for mechatronic systems [26], especially the inverse compensation control based on NN model [27][28][29]. In [30], a polynomial fitting model based on NN was proposed to describe the inverse dynamics of hysteresis in PEA.…”