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

Abstract

Piezoelectric actuators (PEA) are frequently employed in applications where nano-Micr-odisplacement is required because of their high-precision performance. However, the positioning is affected substantially by the hysteresis which resembles in an nonlinear effect. In addition, hysteresis mathematical models own deficiencies that can influence on the reference following performance. The objective of this study was to enhance the tracking accuracy of a commercial PEA stack actuator with the implementation of a novel approach which consists in the use of a Super-Twisting Algorithm (STA) combined with artificial neural networks (ANN). A Lyapunov stability proof is bestowed to explain the theoretical solution. Experimental results of the proposed method were compared with a proportional-integral-derivative (PID) controller. The outcomes in a real PEA reported that the novel structure is stable as it was proved theoretically, and the experiments provided a significant error reduction in contrast with the PID.

A Review of Modeling and Control of Piezoelectric Stick-Slip Actuators

“…Experiments verify that the neural network self-tuning controller has a good track tracking effect [59]. Napole et al proposed a new method combining super torsion algorithm (STA) and artificial neural network (ANN) to improve the tracking accuracy of high voltage stack actuator [60]. Lin et al proposed a dynamic Petri fuzzy cerebellar (DPFC) model joint controller for magnetic levitation system (MLS) and two-axis piezoelectric ceramic motor (LPCM) drive system, which is used to control the position of MLS metal ball and track tracking of the two-axis LPCM drive system.…”

Piezoelectric Actuators

“…ferroelectrics, represented by donor-doped PZT, are usually described by assuming disordered defects as shown in Figure 1b. Hard-soft transitions, induced in hard-type compositions by disordering the otherwise ordered defects, can be achieved by either electricfield cycling [9,60] or quenching [30,61,62].…”

High order sliding mode based drift algorithm for a commercial PEM fuel cell system

“…Authors dealt with LQR parameter tuning through the interval strategy but it increased the complexity of the algorithm. The reviewed background showed that an important detriment of linear approaches is the finding of parameters that could suit in each case; but also, applied linear control may induce the instability because of system uncertainties [22]. Moreover, boost converters have a non-linear behaviour as a consequence of the switching nature [23].…”